Did the Apple IOS 14.5 update kill Paid Social?

Using e-commerce analytics to navigate social tracking issues.

What was the update? 

A year ago as part of Apple's data privacy measure they introduced a new IOS update that has since made advertisers everywhere shudder in dismay. The update in question requires that all mobile apps notify customers upon sign up that their data will be tracked  - and give them the option to opt out.

So while Facebook was once able to effectively track conversions from Paid Social through their title platform as well as through Instagram, this update threw a major spanner in the works when it came to marketing attribution, targeted ads and personalisation as we know it.  

What does it mean for brands? 

From an industry perspective this update (amongst others such as Google’s impending cookie recall) reflects a growing shift towards a digital landscape where user-privacy is king. 

Today only 25% of consumers worldwide are opting in to being tracked. Which means there is a huge amount of people that Facebook can no longer track the behaviours of, let alone whether a conversion has been made. This has a knock on effect of rendering Facebook Ads reporting less than accurate. 

For those who have prioritised Paid Social, times are hard. Because the inability to track these social journeys makes justifying ad spend increasingly difficult, as the data in Paid Social platforms show performance has taken a nosedive. It’s no wonder this situation has left marketers questioning whether Paid Social is in fact dead. 

What does this look like in practice?

Interestingly many e-commerce businesses saw a direct correlation between covid lockdown periods and their social ad revenue increasing (Read about how this occurred in the e-commerce pets industry here). Which meant that performance was hit particularly hard when lockdown lifts also coincided with the IOS14.5 update. As a result many social media advertisers have lost nearly all context for who, how and when customers interact with their campaigns, as well as the ability to effectively refine targeting and messaging. 

While Facebook has done its best to mitigate these tracking issues by using its own AI to predict how likely it is that a conversion happened, its blackbox nature makes these findings dubious at best. The general consensus has become that due to these limitations Facebook tracking data is no longer reliable. 

As a result we have seen many companies moving their spend to other social platforms TikTok, Pinterest and Snapchat, that were less popular prior to the update. Because despite being subject to the same opt in rules, their performance hasn’t experienced the same level of dropoff as Facebook and remains far cheaper.

How do you navigate these issues?

Naturally Facebook’s AI reporting massively overestimates its conversions because it is in the platform’s interest to showcase its value. This is why implementing an e-commerce analytics platform that ingests other sources of data is the most effective way to get around this update issue. For example Conjura pulls in Shopify data instead of Facebook conversions which means that we track the actual transactions and attribute them without agenda.   

Industry benchmarking data can also be invaluable when contextualising the performance of your social channels as it will justify whether it is an internal or marketwide issue. Once performance analytics are ticking along, the strongest customer acquisition strategy will rely on a robust opt-in strategy with customer consent taking its place as a core pillar of e-commerce success moving forward. 

Contact our team here to learn more about working around this update.

What is Product Clustering & why it's important for your e-commerce analytics

Learn how product segmentation combined with e-commerce data analytics provides deeper insights.

The shift towards e-commerce in its many forms has meant that retailers are generating an increasing amount of data about who buys what. This segmentation of consumers is a mainstay of retail marketing that is traditionally broken down by demographics, geographics, psychographics and behavioural data. While this is a great starting point for e-commerce brands to understand who their audience really is, it still fails to acknowledge the products that those individuals are purchasing.

Why is Clustering important?

Although the tracking of stock level, AOV and margin is far from groundbreaking, the combination of these KPIs with granular product data is still a largely untapped e-commerce resource. This type of product data looks at factors such as seasonality, days on sale, and average SKU availability, which provide another layer of insight into how customers engage with the products they buy. As well as helping retailers to understand the true utility and profitability of each product.

To combine this data we use a data science model known as Clustering, which groups together products that share strong correlations in performance. This can reflect wider, more complex trends that would otherwise be hard to spot. For example “Try out” products which offer high margins but are associated with first-time customers who are unlikely to repurchase. Or “Speedy summer” products that sell well at a very specific time of year, but also have high turnover rates. By clustering their product data brands can leverage highly specific insights into the behaviours associated with each of their products, allowing them to build greater efficiencies and maximise growth.

What insights do retailers get from Clustering?

If we apply these clusters to a fashion retailer example, we might find that accessories are a great example of a “try out” product, because they tend to be lower in price, making them an ideal low commitment purchase for first-time customers looking to experience a brand. But customers who choose to purchase an accessory first are also less likely to return to make additional purchases. Fortunately by identifying products that are associated with undesirable outcomes, you have an opportunity to proactively nurture the customers they bring in.  Because with the right follow up marketing, these same customers could be persuaded into purchasing tailored clothing next time - which is associated with repeat purchases and strong loyalty!  

The trends highlighted by these clusters will continue to evolve in tandem with customers’ behaviour allowing retailers to decide which products to discount, discontinue or push to the forefront of their marketing campaigns.

The trick is simply to keep monitoring your clustering analysis and change course when appropriate. This is to say that just because “Speedy summer” products sold well last summer, doesn't mean this performance will be replicated next year and such assumptions could skew product forecasting and have a negative impact on your bottom line. 

Read more about Clustering in a recent Forbes article by our CEO, Fran Quilty.

Five Bullet Friday: Karen Sheehy

Meet Karen Sheehy, mother, podcast-lover and IT Coordinator at Conjura.

We Raised €15 Million In Series A Funding 

We raised €15 Million in Series A round to expand our next generation e-commerce analytics.

For the past few years, our team has been diligently perfecting the next generation of e-commerce data analytics to equip businesses with enhanced visibility across their entire operations through a single intuitive cloud-based platform - Conjura

This category pioneering solution eliminates the need for multiple analytics tools across different business functions, by seamlessly combining data from fulfillment, warehousing, and supply chain sources, with online/offline sales, marketplace transactions, and customer metrics to increase overall efficiency across departments.

Today, Conjura is thrilled to announce that we have raised €15 Million in Series A funding, co-led by Act Venture Capital and MiddleGame Ventures with participation from Tribal VC. This investment will be dedicated to further enhancement of our e-commerce solutions, primarily through product development as well as expansion across the UK, Ireland and several international markets.

General Partner at MiddleGame Ventures, Patrick Pinschmidt expressed his excitement for the partnership stating that “Conjura is redefining e-commerce analytics by solving real pain points for the sector, paving the way for a host of innovative solutions to drive more efficient performance through actionable data and integrated services on top of its leading technology”. While Debbie Rennick, General Partner at Act Venture Capital highlighted “[Conjura’s] strong growth to date as a testament to the market opportunity and the real need for [their] approach”.

Notably consumers last year spent over $175 billion online with UK merchants alone, and those sales are expected to grow by more than 10% in 2022 - representing a market value of at least $200 billion and highlighting the urgent need for businesses to maximise performance to differentiate themselves from their competition. 

Through the Conjura platform, our CEO and co-founder Fran Quilty notes that “we are providing the tools businesses need to enhance their operational efficiency, as well as ensure they are providing the seamless digital experience today’s consumers expect”.

With a holistic combination of daily reporting, data science, and benchmarking data, Conjura is leading the industry towards a more integrated approach to e-commerce data analysis by working with fast-growing brands such CurrentBody, Naturecan, Wild, Saint+Sofia, Percival and CleanCo among others, to set a new, higher standard for performance analytics.

For more detail on our Series A funding round read the full press release here.

Five Bullet Friday: Bálint Biró

Meet Bálint Biró - Part-time game developer and Full time Principal Engineer at Conjura.

- What I’m reading / watching / listening to:

I'm a sucker for a good fantasy novel and my all time favorite is The Kingkiller Chronicle by Patrick Rothfuss. It's a trilogy (though the 3rd book hasn't been released yet) that's designed to be read multiple times. There are hidden secrets and nuggets all around just waiting to be found. I'm on my 15th or 16th reread this time around...Just hoping that we'll get the third book at some point!

I also spend quite a bit of time just reading cryptocurrency whitepapers (Bitcoin, Ethereum, Solana, etc) and tech blogs (mostly the blog from Ethereum founder Vitalik Buterin and IOHK (Cardano) research papers). I'm trying to gather a better picture of the utilities of crypto and how we - as a society - might leverage it, all the while navigating through the complex ethical issues it presents.

- Interesting work happenings: 

Having spent the last three months building out the first version of our automated e-commerce benchmarking offering, I'm super excited to see how we will evolve in the next few months. All I can say for now is that things are looking rather interesting!

- Work shoutouts: 

I'd like to give a massive shoutout to the whole Benchmarking team for doing what seemed impossible a year ago. It's been a gigantic undertaking going from a bespoke, service-based consultancy to a standardised and automated e-commerce focused SaaS product. Ultimately through years of hard work we’ve been able to identify the overlap of what every e-commerce brand needs to grow consistently and we’ve had loads of learnings along the way!

- What I’m learning about:

As of recently I got intrigued by the idea and utilities of decentralised networks going way beyond just cryptocurrencies. I even started a blog series on exploring how we could actually harvest this technology and why the current implementations are, in my opinion, the incorrect way of going about them. I hit a bit of a hiatus in recent times due to major life events, but I'm now getting ready to get back into it and explore it further.

- People who are inspiring me:

To be honest, I find all kinds of people inspiring. Whether it's the single mother working multiple jobs to provide for her kids, or the 5 year old doing his best to learn what 1+2 means. As long as someone has an insatiable curiosity and drive, I'll feel inspired to be around them.

Five Bullet Friday: Nicky Moorhead

  1. What I’m reading, watching and listening to:

    When it comes to reading, I tend to find a topic of interest and then spend the next few months reading exclusively around it. Lately, that topic has been financial markets and investing although by reading, I really mean listening to an audiobook when I’m out walking or driving! In recent weeks though, I've been enjoying the silence of not listening to anything so I should use this as a prompt to reignite my Audible crusade.

    I'm in desperate need of a new series to get my teeth into, I feel like I’ve had too many false starts recently but I am eagerly anticipating the release of part 2 of Ozark series 4 even if it’s been so long since I watched the first few series that I spend most of my time trying to get back up to speed with who all the characters are.When it comes to music I’m terrible for finding an artist and then listening to them until I literally know every song backwards. I’ve got two of those artists at the minute; Ryan McMullan and Jack Curley. If you haven’t heard of them, be sure to check them out.

  2. Interesting fact about me:

    You’ve got me on this one. I once played golf with Rory McIlroy, does that count?

  3. Interesting work happenings:

    As I’m new to Conjura, the last few weeks have been a steep learning curve. I’ve been trying to get myself up to speed with our product, our sales process and everything in between. Outside of that I’ve built an RoI model/calculator for prospective customers to try and help us illustrate the value of using Conjura and unlocking the insights that their data holds. I’m really excited about it. 

    A big shout out to the whole commercial team for making me feel so welcome from day one. A special shoutout to Colm Kelleher and Will Caffrey for their time and patience in helping to bring me up to speed with our product and Karen Sheehy for helping to get me setup remotely on my first day, the process was literally flawless.

  4. What I’m learning about:

    I haven’t started just yet but I’m keen to learn more about DBT and SQL. I’ve been very consumed in the Google suite over the last few years and so Google Apps Script and Query have been my go to but it’s definitely time to learn something new within data analytics. I’ve just reread those last two sentences and realised I’m incredibly boring and should have made something up like I’m learning to tightrope!

  5. People who are inspiring me:

    The people of Ukraine. Their courage in the face of adversity is remarkable.

Me vs. The Market: What E-commerce Verticals Are Most Attractive?

Our monthly Me vs. the Market segment pulls data from 1000s of businesses to provide key insights for e-commerce professionals and industry observers looking to benchmark and understand the digital commerce marketplace.

Key definitions:

  1. Customer Acquisition Cost (CAC): the amount it costs to acquire a new customer.
  2. Average First Order Value (AFOV): average amount spent on a customer’s first purchase.
  3. Customer Lifetime Value (CLV): average amount that a customer spends over the first 12 month period.

This analysis will look at the performance of the most common e-commerce verticals over the last 24 months. In reality this is very complex to measure and involves considering gross margin, return rate and customer acquisition costs.

To simplify this matter we will examine how the core purchasing characteristics of each vertical (i.e CLV, AFOV & CAC) intersect and what that says about a vertical’s “attractiveness”. This information may be helpful for CEOs or investors looking to better understand the market.

First Purchase Benchmarks

First we look at the relationship between AFOV and 12 monthCLV. Verticals such as Sports and Home & Garden need to make a profit on every transaction as their 12 month repeat purchase rate is low. Assuming a gross margin of 50% the high AFOV still allows some wiggle room for shipping costs and marketing costs or customer acquisition costs.

A high 12 month repeat purchase rate is favourable for low AFOV businesses as it reduces the reliance on being profitable from the first purchase.

Verticals such as Pets, Beauty and Food & Drink benefit from higher repeat purchase rates than Fashion, Health & Nutritionand Toys & Hobbies as shown in Figure 1 below.

Change in CAC Benchmarks

Now we take a closer look at the 3 verticals with the highest repeat purchase rate Pets, Beauty and Food & Drink and examine how their cost of acquiring customers has fared over the past 15 months.

We can see from Figure 2 that CAC is getting more expensive across all 3 verticals. This suggests that the dynamics of a good e-commerce model is now more widely understood, resulting in lots of new challengers entering the market and driving up CAC for all.

As seen in the industry

Most founders want businesses that have a repeat purchase nature, hence the popularity of our top 3 verticals.

We see this occurring with businesses such as Beauty Pie, a buyer’s club for high-end beauty and wellness products have just raised an additional £100 million boost. Beauty Pie’s founder Marcia Kilgore has said that its customer retention rates are currently higher than Netflix’s & Spotify’s, and 20x higher than other D2C beauty companies, partly a result of the buyers’ club model.

Another example is Butternut Box, a London-based fresh dog food business with a tech-driven platform that delivers HelloFresh-style, catered dog food.They recently raised an additional £40 million to scale their business. Again, in this example repeat purchase rates are likely to be high due to the brand’s subscription model. Butternut Box competes with the likes of Lily’s Kitchen, Tails.com and Natural Instinct for this lucrative new market.

Me vs. The Market: Black Friday Is Not the Investment It Once Was

Our monthly Me vs. the Market segment pulls data from 1000s of businesses to provide key insights for e-commerce professionals and industry observers looking to benchmark and understand the digital commerce ecosystem

Key definitions:

  1. Return On Ad Spend (ROAS): the amount of revenue earned for every dollar spent on advertising
  2. Customer Acquisition Cost (CAC): the amount it costs to acquire a new customer.
  3. Customer Lifetime Value (LTV): average amount that a customer spends over the first 12 month period.
  4. LTV:CAC Ratio: Customer lifetime value vs. Cost of Acquiring Customers. In other words how profitable a customer is over their lifetime.

Black Friday – you know the deal. It’s an event with a reputation for huge discounts and hoards of new customers, not to mention it’s shelf clearing potential. In truth, e-commerce businesses are no longer getting the same bang for their Black Friday buck.

This is due to an unfortunate combination of rising CAC during this period as well as consistently poor repurchase rates from the customers it acquires. While businesses can still enjoy a healthy revenue bump from this event, as a long-term strategy for growth Black Friday is simply not the investment opportunity it used to be.  

This analysis looks at the performance of Black Friday campaigns (from 19-30 November) for e-commerce companies across multiple categories between 2018 – 2020; by examining CAC, ROAS and LTV for customers acquired during this period. 

Average return on ad spend (ROAS) for Black Friday has decreased over the past three years, falling from 46.1% higher than other times of year in 2019 to 28.7% in 2020.

Despite this downward trend, Black Friday is still worth investing in as it’s responsible for approximately 8% of revenue for the year with revenue peaks 3 – 4x bigger than the average day.

So while Black Friday has the potential to drive excess revenue for businesses, that payoff is decreasing over time. 

With customers primed to pounce on these seasonal discounts, Black Friday through Cyber Monday, can be a powerful period for acquisition. However, customer acquisition costs (CAC) for this week were also 13.3% higher in 2020 than other times of the year (Calculated on Paid Search and Social), with all businesses vying for the same Black Friday bargain hunters.

With the pandemic paving the way for more e-commerce businesses to take root, and auction based ad platforms thriving off the increased competition, you can bet your bottom line that each click will cost more than the last. This increased cost of acquisition may be worth it if the new recruit becomes a loyal customer. If they don’t, the investment has been costly.

Although Black Friday is a powerful tool for attracting new customers – all customers are not equal in value. In fact, these seasonal discount-driven buyers have a 9% lower 6 month LTV than those acquired during the rest of the year, meaning they have a tendency to quickly burn and then churn. 

Their repurchase rates are also consistently 5% worse depending on category and the gap is widening as this continues to decline. By analysing previous customer data retailers can predict their likely return on investment from these customers before deciding how heavily to discount items and improving profit margins as a result.

The Black Friday event is becoming less worthwhile to invest in, with a worsening trend in LTV:CAC ratio for all paid channels. In other words, this event brings in poor quality customers, who do not come back to purchase again, and are also increasingly expensive to acquire in the first place.

As customer buying intent is high during Black Friday, we don’t advise withdrawing from this activity altogether, but we do suggest you proceed conservatively and measure aggressively during this period.

How to approach Black Friday

Black Friday is becoming less and less valuable as acquisition costs soar and new customers fail to buy another day. To add insult to injury, this Black Friday amidst the continuing pandemic, chaos of Brexit, supply chain disruptions and inflation, customers are still expecting compelling discounts.

Given these circumstances and their negative impact on CAC, retailers should use discounts to strategically clear shelves of aging or unpopular stock ranges. They might also try additional cost-free incentives such as the doubling of loyalty points for purchases made on that day. 

It’s hard to say when Black Friday will generally become more hassle than it’s worth and likely impossible to judge its viability before plopping down your hard earned marketing budget. The only way to stay ahead of this downward trend is to keep a firm eye on your ROAS, CAC, LTV and of course your LTV:CAC ratio. 

At the end of the day, Black Friday is still an undeniably important event for bringing in new customers and their cash; presenting an opportunity for driving retention as well as short-term revenue goals.

As an e-commerce performance accelerator, Conjura helps brands to identify, understand and execute on their most impactful opportunities by analysing business performance against industry benchmarks to inform strategy and drive growth.

Through our lens, companies can quickly and easily understand whether their approach and spend for events such as Black Friday are driving OR draining their business of its lifeforce!

Online Marketing: Customer Lifetime Value & Return on Ad Spend

One of the most common questions we are asked is “How do I make sure I am maximizing the value I get from online marketing?”.

Given how most companies spend at least 6 figures a year online marketing, this question is fundamental to the overall performance of the whole company, not just to the performance of the marketing team. Without a framework to provide feedback and focus for your online marketing efforts, all those marketing dollars could be going to waste.

In this article, we will discuss two frameworks that can be used to devise your online marketing strategy and to interpret the daily feedback that online marketing data can provide. The frameworks focus on optimising your marketing performance towards return on ad spend or customer lifetime value.

We’ll define the components of each framework and outline how each can be used to help you allocate budget, assess performance and drive online marketing expansion. Finally, we’ll provide some clarity over how to decide which method best suits your business.

The Frameworks: Customer Lifetime Value & Return on Ad Spend

In principle, the frameworks are very simple. They both involve generating actions based on how one metric looks compared to a target value for that metric. Where it gets difficult is in calculating the metrics themselves as well as the targets.

There are various pitfalls that can affect their accuracy and interpreting the results and generating actions is not always straightforward. We hope this article will address these difficulties.

Before continuing it is important to note that the frameworks do not attempt to answer all the questions that online marketing will raise. They cannot answer questions like “How can I increase revenue from my paid search activity?” or “How can I improve the conversion rate of my paid social activity?”.

Instead they can help marketers choose what performance to expand, reduce or cut completely. If a given activity meets the criteria of the framework, then you should continue that activity until it does not meet the criteria.

The frameworks are intended to provide a focus for your marketing efforts. The goal is to make it very easy to decide whether a given marketing activity is worth pursuing. In a world with limited resources, focus and prioritisation of the most important tasks is what sets apart the best from the worst performing companies. That is the power of these frameworks – clarity of thought and action.

Success in Online Marketing

Before diving into the frameworks, we would like to address what Conjura considers to be non-negotiable traits when approaching online marketing; consistency of application and making data-driven decisions.

Success in online marketing is ultimately a data problem. Marketing online generates a lot of data; in the ad platforms themselves, in tools like Google Analytics and transactional and customer data. Making sense of it is key to making effective decisions.

Once plans have been made, a framework chosen and a strategy devised, it must be consistently applied to all channels and campaigns. If it is not applied consistently, it will not be possible to determine whether it is working. There will be too many caveats and confounding factors that make it difficult to have confidence in any inferences made.

Both methods outlined below focus on defining targets and objectives and ruthlessly cutting any activity that does not meet these targets. Without proper planning, data and consistently sticking to a strategy, these frameworks will not be effective.

Now let’s turn to the frameworks themselves.

Framework 1: Return on Ad Spend

Return on Ad Spend, or ROAS, is a calculation of the amount of revenue you receive in return for spending £1 on marketing.ROAS = [Revenue] / [Online Marketing Spend] A high ROAS indicates that a given marketing channel generates a lot of revenue for a relatively low marketing spend. A ROAS of less than 1 indicates that a company spends more than is generated in revenue.

Calculating Return on Ad Spend

Calculating ROAS appears easy at first glance. But there are several factors that must be considered before it is possible to accurately calculate the metric; the attribution model used, and the quality of tracking implemented on your marketing activity.

Marketing Tracking refers to the practice of adding a snippet of text to the end of a URL to highlight what campaign and channel a click or visitor came from. The most common format used for tracking your marketing activity is that used by Google Analytics – UTM tags [link to UTM tags page].

Adding UTM tags to your URLs enables Google Analytics to categorize traffic into channels and campaigns. This then allows Google Analytics to perform effective attribution and enables users to properly assess the performance of their marketing activity. If you do not effectively track your campaigns, you cannot perform any kind of performance assessment and you are operating blindly.

Having an extensive background in marketing analytics, tracking is very close to Conjura’s heart. There are two characteristics that signal effective marketing tracking; consistency and granularity.

Consistency – all campaigns must be tagged, and all tags must follow the same naming convention and tag format. It is imperative that time is spent devising a comprehensive and future proof naming convention.

Granularity – tracking should include as many details about the campaign as possible. The more information included, the easier it is to determine the objective, target demographic, location etc. of the ad and the easier it is to analyse.

Attribution is a hugely complex topic that has taken up thousands and thousands of words. As such, we are not going to delve deeply into attribution in this article. Instead, we’ll direct you to Neil Patel’s article on attribution. It gives an overview of various models and how to decide which one is best for your business.

Conjura suggests approaching attribution pragmatically – simply choose a model that works for you and stick to it. The simpler the model, the easier it is to understand. Sometimes, this is the best approach.

Only by maintaining a consistent model can you assess performance over time and across channels. Additionally, if everybody in the business can understand the model, and its limitations or idiosyncrasies, then everyone can have an informed and productive discussion about what is and is not working in your marketing activity.

Using the Framework

Before using Return on Ad Spend to drive decision making, you must develop a ROAS target. Ideally you will have specific targets for each channel and even at the campaign level.

Defining Target ROAS When defining target ROAS, it is important to account for the attribution model being used, especially if you are setting targets at the channel level.

For example, imagine an Ecommerce company using Facebook campaigns to build awareness and using Google Shopping to convert leads into customers. Brand awareness activity drives leads into the top of the conversion funnel while Google Shopping converts leads at the bottom of the funnel.

If this company is using a last click model, it will assign all transactions to Google Shopping ads, likely leading to a healthy ROAS figure. Meanwhile, the impact of the Facebook’s brand awareness ads is relatively ignored, resulting in a poorer ROAS figure. In contrast, a first click model will assign more transactions to Facebook and less to Google Ads, affecting the ROAS on each channel. When defining a target ROAS, it is very important to adjust it for the attribution model you are using.

Additionally, when defining ROAS, it is important to ensure that the target chosen results in a profitable outcome. This means choosing a ROAS that covers costs of doing business such as product and other operating costs.

Finally, being able to use historical data or, even better, industry benchmarks for ROAS targets is very beneficial. If you know what “good” looks like, you should strive to emulate and outperform it.

Using the ROAS Framework It is understandable to think about ROAS as “the higher, the better”. This is a relatively limited view, however and can result in missed opportunity. To explain this further, we’ve outlined three scenarios below.

Imagine a company sets a target ROAS of 5 for its paid search marketing activity. The company must assess the actual ROAS achieved compared to this target. Consider the following scenarios for the actual ROAS achieved:

Actual ROAS is 3

This is an easy one – the marketing activity has not been successful. The company is not generating the level of return required given the amount of money they are spending. In this scenario, breaking the paid search channel down into campaigns and reassessing ROAS is a good starting point.

Is there a group of campaigns where ROAS achieved is above target for example? What makes these campaigns better performing than the rest? Is it feasible to reallocate some budget from underperforming campaigns into better performing campaigns?

There are a whole range of causes of underperformance in this scenario such as keywords being very competitive and expensive, the marketing channels in use driving poor quality traffic or poor website conversion rate.

Actual ROAS is 5

Again, interpreting this is easy – the activity is successful and on target. The challenge when you are meeting targets is scaling your activity. The key question is whether you can increase spend and expand activity while maintaining the same level of ROAS. This is the sign of a great online marketing team.

Of utmost importance in this scenario is experimentation. This entails developing new ideas and allocating a small amount of budget to determining whether these ideas show promise. If they do, allocate more budget. Then iterate repeatedly while always monitoring how actual ROAS compares to your target.

Actual ROAS is 7

This is probably the most interesting scenario, but also the one that most of Conjura’s customers find difficult to accept. For Conjura, in this scenario the marketing activity has been unsuccessful, despite actual ROAS being greater than target.

While the performance appears good at first glance, the company has left additional revenue on the table – it has not met its full potential. The laws of diminishing returns apply to ROAS; ROAS tends to fall as spend increases. This scenario shows that the company has room to scale spend and expand activity. The company should keep spending until ROAS reaches target.

The above scenarios show how important having a target is when using ROAS. Without that target, the company will not know how to interpret the figures and will struggle to implement a successful strategy.

Framework 2: Customer Lifetime Value

Customer lifetime value (LTV) is the value of a customer to your business over their “lifetime”. The definition of a “lifetime” can vary but is usually somewhere between 12 – 48 months.

There is no right or wrong answer in what you consider the most appropriate lifetime for your business. It is more important to be consistent with your choice so you can monitor whether LTV is changing over time and make efforts to identify why.

Calculating Customer Lifetime Value

Calculating LTV is straightforward, simply calculate the total value of a customer over their lifetime. Value in this instance could be revenue, but it is very important to account for discounts, returns and shipping. Even better is to define value as the margin generated by each customer.

Often, data availability and difficulty in calculating margin at a customer level makes generating LTV time consuming. What’s most important is that a consistent definition is used, even if it is based simply on revenue.

Using Customer Lifetime Value

When using customer lifetime value to assess online marketing performance, Conjura always recommends considering cost per acquisition at the same time. Specifically, the relationship between these metrics will provide the insights and actions required to maximize performance under this framework.

It is necessary to first define what this relationship should look like. This involves developing your investment principles. These are the rules and requirements by which you invest your marketing budget. If your activity meets your investment principles, you are succeeding. If it does not, you are failing. We will discuss investment principles later in this article. First, we will discuss cost per acquisition.

Cost per Acquisition

Cost per Acquisition, or CPA, is one of the most common metrics used to judge your marketing performance. Its calculation is straightforward:

CPA = [Online Marketing Spend] / [# Customers Acquired]

Simply put, it tells you how much it costs to acquire one new customer.

Calculating Cost per Acquisition

Like calculating ROAS, calculating CPA is dependent on the attribution model is use. In this case, the attribution model determines which transactions and new customers were attributed to which channel, so it has a huge impact on CPA per channel. Additionally, how companies identify new customers from existing ones is very important.

The only way to know whether a transaction was made by a new customer is to deduplicate your customer base. This means assigning an ID to each customer and then linking every transaction made by that customer to the ID. This allows you to determine whether a transaction was made by a customer that exists in your database already. If they do not, they are a new customer. If they do, they are an existing customer.

There are many methods for customer deduplication including algorithmic fuzzy matching and points-based systems. If you’re limited for resources, a good starting point is to use the customer’s email address. This won’t be perfect, but Conjura has found it gets customers 90% of the way there.

Investment Principles

As explained above, investment principles are the rules by which you allocate budget across channels and campaigns. They define how the relationship between LTV and CPA should look if your activity is successful.

Investment principles determine the desired payback period for your marketing spend. To put it another way, investment principles establish when a company wishes to break even on its marketing spend. For example, if a company sets a 12-month investment principle, the marketing budget should be invested in such a way that if the company spends £10,000 on marketing in January, it expects to have returned £10,000 by the following January. Anything after the following January, will be profit.

What is the correct investment principle? There is no one size fits all answer to this question. Instead it is a decision a company will make based on how aggressive it wishes to be, it’s risk appetite, cash reserves and the industry it operates in. For example, a 36-month investment principle is aggressive. It means that the company is willing to make a loss on every customer for the first 36 months of those customers’ lifetimes. In contrast, a 12-month investment principle means that the company wishes to break even on a customer after 12 months, which is much less aggressive.

In new industries, where there is not a clear market leader, companies may wish to be more aggressive in order to grow marketing share. They are willing to carry the loss for longer. This will likely lead to a longer investment principle. In more established industries, investment principles might be shorter as companies wish to maximise their value. A longer investment principle is also likely to be chosen by a company with high cash reserves as these companies can absorb the initial loss on a new customer for longer.

Whatever investment principle is chosen, it is extremely important that the company then calculates the corresponding LTV e.g. if choosing a 36-month investment principle, the company must calculate what the 36-month LTV of their customers is.

Doing this enables the company to set target CPAs for their marketing activity. In order to determine how well you are performing, you should compare actual CPA with your target CPA. By using LTV to set a target CPA, and then monitoring the actual CPA, it is possible to regularly determine whether your marketing activity is profitable and successful.

Ideally, investment principles and target CPAs will be applied per channel and, if possible, at the campaign level.

This is best explained by way of an example. Imagine a company wishes to break even on a customer 24 months after that customer is acquired. The 24-month LTV is £200. Therefore, the target CPA for this company is £200. The scenarios below outline how to use and interpret the relationship between actual CPA, target CPA and LTV.

Actual CPA is £250

In this scenario, the company is spending more to acquire a customer than that customer is worth after 24 months. The company is not successful considering its investment principle. In this scenario, it is advisable to investigate those channels and campaigns at the root cause of this problem. As with the ROAS framework, it is advisable to dive into the campaign level data and understand the relationship between actual and target CPA. Doing this enables marketers to identify and focus on the worst performing campaigns and make relevant changes.

Is the issue caused by poor quality traffic, poor retention, poor conversion rates for example? Regardless of cause, spend should be reduced and reallocated to better performing campaigns, at least temporarily until solutions can be implemented and tested.

Actual CPA is £200

In this scenario the company is meeting its investment principles. It is acquiring customers at a cost that will allow it to break even after 24 months. Like the ROAS example, the challenge is in scaling spend and activity while maintaining this performance.

Actual CPA is £150

The company has acquired customers for less than the target CPA, meaning that the company will break even on these customers sooner than 24 months. This seems like a positive result; however, this means that the company is not reaching its potential – it could have spent more on marketing, acquired more customers and remained within the parameters set by the investment principle.

CPA tends to have a positive relationship with spend – as spend increases, so does CPA. Therefore, the company should increase spend and activity until the actual CPA rises to meet the target. At this point, the company will have reached its limit on this channel and it may be time to start investigating other promising channels


The great thing about these metrics is that most companies can and should focus on only one. If interpreted and used correctly, they will instill discipline in spending and optimization decisions and provide a clear set of actions to take in each scenario.

So, how do you decide whether ROAS or LTV is the best framework for you?

Lifetime value and ROAS are both measures of profitability. The main difference between them is their time horizon. ROAS covers profitability on a single transaction i.e. the company spends £10 to generate a transaction of £20 resulting in a ROAS of 2. In contrast, LTV covers a longer time horizon i.e. the length of a customer’s “lifetime”. As discussed previously, this time horizon for LTV can vary, but is generally at least 12 months.

The choice of which one is more appropriate is dependent on a key factor – purchase frequency.

Simply put, a company that receives a relatively high purchase frequency e.g. 3 purchases per year over multiple years – will be more interested in assessing marketing performance based on the LTV framework. Customers are more likely to stick around for more than one purchase and this should be considered when assessing marketing performance.

Using ROAS in this case may limit what a company believes they can spend, especially if the item price is relatively low, which it likely is if customers have a higher purchase frequency. This limits their ability to compete for new customers and negatively affects overall performance.

In contrast, companies with a low purchase frequency – mattress companies for example – do not expect many future purchases from customers. Therefore, the lifetime of a customer is usually one purchase. In this instance, ROAS and LTV will be very similar, especially if ROAS accounts for margin. Companies in this position should save themselves the trouble of calculating LTV and use ROAS.

There are many characteristics that can impact purchase frequency such as the opportunity to cross sell and how long the product remains useful to the customer, but these are best covered in a separate article.


The return on ad spend and customer lifetime value frameworks for assessing marketing performance are very straightforward to understand and implement. That’s what makes them very powerful. It is much easier for a marketing team to get behind and implement a simple framework. That said, the frameworks are not without difficulties and pitfalls, especially when combining the data sets required and calculating the metrics involved.

In order to get the most from whichever framework you choose, it is imperative that every decision made is made with your framework in mind. If something does not fit, stop doing it. If something fits, continue or try to scale that activity to maximise performance.

UTM Parameters: Unlock Great Online Marketing Performance

UTM parameters are a well known concept in online marketing, but their use rarely follows best practice. By not using UTM parameters properly, companies are missing a trick and are likely not meeting their potential online.

Conjura wants to enable businesses to maximise their performance online and UTM parameters are central to that. With that in mind we’ve put together this guide to using UTM parameters effectively. We start off with a quick background into UTM parameters and how they work, before diving into the details around how to use them effectively and how to design your tracking convention to get the most out of your data. 

UTM parameters, what are they and how do they work?

A UTM parameter is a string appended to the end of a URL that helps marketers track where visitors to that URL are coming from. The URL in this case is usually one used in an online ad, placed in an email or on a blog. UTM parameters help marketers split website traffic among channels and campaigns so they can understand whether their online marketing activity is working. 

Without UTM parameters it is almost impossible to determine where visitors are coming from or which channels are good at acquiring customers. Without them, it is not possible to make good decisions about where to invest in online marketing activity. Without UTM parameters, the marketer is effectively flying blind.

As outlined above UTM parameters are simply strings of text appended to the end of a URL. They have no effect on the destination of the URL or on any other aspect of the visitor’s experience. They work by “talking” to web analytics tools like Google Analytics. Google Analytics will identify and read the UTM parameters and then use them to determine the source of your traffic. 

There are five component parts to UTM parameters: source, medium, campaign, term, and content. Here is a link to Google’s description of each component part. Source, medium and campaign are the most commonly used, but companies can often get great value out of using the additional parameters too. By varying these parameters depending on where your URLs are placed (i.e. in ad campaigns across different channels), you can gain visibility on click-through rates, conversion rates, costs of acquiring customers and more, at a granular level.

This is the panacea of online marketing as it allows marketers to make good decisions about how to optimise campaign settings and allocate budget based on how campaigns are performing.

How should UTM parameters be used? 

UTM parameters should be used on every single URL a company promotes; both paid and organic. Whether you’re running paid ads on search and social platforms, placing a link to your website within blogs or social media posts, or even using offline methods such as leaflets and billboards, any web address you include should be tagged with UTM parameters.

You can even use UTM parameters to track affiliate or influencer marketing by ensuring the influencer uses a URL in their posts that contains a UTM tag. This holistic approach will empower you to accurately track your performance across all channels, even offline ones, and correctly attribute purchases and other conversion actions to their source.

Google Ads makes it easy to correctly tag ads with UTM parameters. Simply ensure that the auto-tagging feature is turned on. But with most ad platforms you need to manually tag every campaign and ad separately. Sounds like a lot of work, but the benefits outweigh the costs! 

Happily there are a few tools to help you on the way. In particular, Conjura recommends using Campaign URL builder tools. Additionally, in Facebook you have the ability to use dynamic URLs to ensure your ads are tagged correctly.

Unfortunately, despite these tools, in Conjura’s experience most campaigns are not tagged with UTM parameters at all. This greatly limits a marketer's ability to make good decisions. There is simply not enough information about what really is driving traffic and the underlying trends in performance.

Take the example of a company that drives traffic to its website via a large number of Facebook campaigns. By not tagging campaigns properly, all visitors driven to the website as a result of these campaigns are bucketed into one “facebook / referral” channel within Google Analytics. In this scenario the company will not be able to identify the best and worst performing campaigns and therefore cannot make optimisation decisions. 

Of course, some visibility is available with Facebook itself. While Facebook’s reporting is useful, its attribution model can be over-generous as it doesn’t take other marketing touchpoints (e.g. search, display etc.) a customer may have into account.

Additionally, ad platforms like Facebook use cookies and pixels to determine whether a conversion event occurred. While these are useful, they can’t beat using actual customer and transaction data, which is not possible without using UTM parameters to collect the required data. It’s not all Facebook’s fault, it doesn’t have access to this other data, so can’t incorporate it into its reporting. 

Designing a UTM parameter tracking convention

When using UTM parameters, it’s important to follow a good tracking convention. A tracking convention is a list of rules that govern how a company uses UTMs across all its marketing activity. We recommend devising a tracking convention and then sticking to it every time you place a URL, anywhere (across campaigns, channels, blogs, emails etc.). This means there’ll be no gaps in the data you collect and ensures that your data can be aggregated and analysed easily. 

The tracking convention should outline how the business uses the five UTM parameters we outlined earlier across each channel. The great thing about UTM parameters is that they are very flexible and it’s possible to use them in whatever way works for your business. The most important thing is that they are used consistently across your marketing activity.

We have laid out a simple example of a tracking convention below to give you a place to start. 

A convention like the above enriches your Google Analytics data and gives you the ability to slice the data in a variety of ways to gain meaningful insights. No matter what convention you decide on, the data placed in each of the five parameters should be informative when looking at each parameter in isolation as well as when looking at the parameters put together. 

Inspecting the data collected in each parameter separately provides broad insights into how visitors like to interact with your brand. For example, you could pit Facebook against Twitter and compare their overall performance using just the source parameter, or you could determine which bidding strategy is most effective using just the medium parameter.

Similarly you could compare the performance of certain styles of content across all platforms using just the content parameter, answering questions like “How do text-only posts perform when compared with posts that included an image?” or “What image did users respond better to?”.

Meanwhile, conducting analysis on multiple parameters at once (e.g. source, medium and campaign) can give targeted insights into particular ads. The more parameters included, the more specific the analysis. For example, it is possible to answer questions like “How many sales were driven by my Summer Sale email campaign when I also included the image of the beach in the email?” or “What was the return on advertising spend for the Men’s Shoes acquisition campaign that was run on Google search?”. 

It is only possible to answer these questions when a proper tracking convention is followed and UTM parameters are present across all campaigns. 

Next Steps

Maybe you have a strong UTM strategy in place already and if so, great! But if not, it’s never too late to start. Implementing a robust approach to UTM parameters will revolutionise your tracking infrastructure and customer analytics, allowing you to make more informed decisions around your digital marketing spend and strategy. We hope this article gave you some ideas on where to begin, but if you’d like some more advice then don’t hesitate to get in touch!

How Sophisticated is Your Approach to Customer Analytics?

Conjura has worked with all types and sizes of consumer business. We’ve worked with businesses who are just starting on their customer analytics journey as well as businesses who have large analytics teams. We’ve seen a huge range of sophistication when it comes to approaching how data is collected, used and acted upon.

Our experience has allowed us to identify and surface 16 questions across 4 categories that can signal your level of sophistication in customer analytics. In this article, we’ve outlined these questions and how they can help you identify the level of sophistication of your approach.

Data Structure for Customer Analytics

Customer analytics follows the Garbage In, Garbage Out (GIGO) principle, so understanding how you are collecting and structuring your data is critical for success. Ask yourself these questions to identify the data gaps in your organisation.

1. Have steps been taken to improve the quality of data you are collecting?

It doesn’t matter how smart your analysts are, if they work with bad data, they will get bad insights which results in wasted time and resources.

To ensure data analysts are using reliable data, businesses must focus on data collection from all sources including marketing platforms, payment tools, internal databases and tools like Google Analytics. This means using standardized methods for creating and naming campaigns in marketing platforms or reducing the amount of human-inputted forms that are in use (could you use a drop-down menu instead, for example?).

2. Can you extract data from relevant tools in an automated fashion?

All analytics projects require data. In customer analytics, it is likely that relevant data is stored in a cloud-based platform like Google Analytics, Google Ads or Facebook.

In all cases, data must be extracted from these platforms in order to be used by the analyst. Most tools have a manual export functionality. But this is usually very time consuming and often the tool will limit the amount of data that can be exported in one go.

Sophisticated businesses will extract this sort of data via an API. APIs usually have fewer restrictive limits on data extraction and can be automated, but to do this generally requires knowledge of a coding language like Python.

3. Has a data warehouse been created that includes all datasets related to the customer?

To get a comprehensive and objective view of performance or customer behaviour, it is not appropriate to use only one data set from one source. Effective and insightful customer analytics uses multiple data sets.

Conjura believes that the best insights are derived from combining data from marketing platforms (such as Facebook or Google Ads), web analytics platforms (like Google Analytics), customer data and payments information.

The most sophisticated businesses will ingest and store this data in a single, easy-to-use warehouse. The result is that analysts will spend less time data wrangling and more time generating the insights that can change your business.

4. Does a foreign key exist to join data sets together?

Foreign keys are unique identifiers that exist across different data sets that enable them to be integrated as part of an analytics project. They are fundamental to an analyst’s ability to combine multiple data sets and deliver comprehensive and insightful analyses.

5. Have you automated the integration of these data sets?

Not only is it important to use and store multiple data sets in a single, easy-to-use location, the most sophisticated businesses also automate the integration of these data sets.

Integration refers to how the data can be used in conjunction or “joined together“. This is by far the hardest part when it comes to supercharging your customer analytics capability. Most data sources are not designed to talk to one another, so it takes a lot of data manipulation and processing before data can be joined.

This is also the part of an analytics project that takes so long. But by automating this process, a huge amount of time can be saved for every analytics project, thereby increasing the throughput of your analytics team and increasing the volume of insights generated. It also reduces the chances of human error affecting the accuracy of analytics.

6. Do you integrate data at different granularities?

Not only is it important to integrate all your data sets, in order to speed up insight generation and ensure future proofing of your infrastructure, data must be integrated at different granularities.

Granularity refers to the level of detail that your data contains. For example, Google Ads will allow you to extract data at a keyword level, a country level or a campaign level. Each level may be used by a different analysis, but it is important to have the flexibility built into your data infrastructure to handle them all.

This is a very important, but also very difficult step to get right. Given that your business may by using dozens of data sources, there may be hundreds of levels of granularity required to give your infrastructure the flexibility its needs to support highly sophisticated customer analytics.

7. Is there a data dictionary for each source in the schema?

Businesses are in constant flux, whether from a data or a personnel perspective. With that in mind, clear and comprehensive documentation helps to ensure the longevity and usefulness of your data infrastructure. The documentation should cover each aspect we have outlined above to reduce key man risk and to speed the upskilling of new hires.

Using Data for Customer Analytics

Data and analytics can, and should, support decision making across the whole organisation. In this section, we have outlined some key questions to ask to understand whether your business is getting the most from your data.

8. Is your business performance reporting automated?

The most effective companies can make data-driven decisions very quickly. These organisations can assess performance, spot opportunities and act accordingly. This is supported by automated business reporting that is always fresh, accurate and available for use.

9. Can your business quickly and easily calculate cost per acquisition, customer lifetime value and retention rate?

These are the foundational KPIs for any consumer business. Sophisticated companies calculate these daily and will report them to the entire business.

10. Does your analytics capability support customer acquisition and the monetisation of existing customers?

Given the data available, a sophisticated analytical capability will support the development of strategies to both acquire new customers and expand or retain existing ones. By failing to utilise analytics for these purposes, companies are at a disadvantage to their more analytically savvy competitors.

11.Does your analytical team focus solely on historical insights?

Conjura endorses data foresight in addition to data insight! Generating insights on historical trends is very valuable. However, a sophisticated analytics capability will also use their data to make predictions on the future value of their customers, on their stock requirements, on churn likelihood and so on. This sort of analytics enables a business to be proactive, rather than reactive.

12. Are your predictive models deployed into production?

A predictive model can be very informative at a single point in time. However, the insights gained from single-use models become out-of-date very quickly as data and customer behaviours change. The most informative predictive models are deployed into production, meaning they are automated to run every day and they are regularly maintained and tweaked to ensure that their insights remain relevant over time

Technology for Customer Analytics

13. Is your organisation using the most innovative data infrastructure tools and processes?

The amount of data available to companies is massive and is growing rapidly. Legacy systems are not designed to deal with the scale of the data available and will limit your analysts from unlocking the value of your data.

By using modern data infrastructure such as those offered by Amazon, Microsoft or Google, companies are putting themselves in a good place to future proof their capability to continue to generate value from their data. The open source movement has also transformed data analytics. With the availability of libraries in Python more and more functions are possible with less code provided you are using the best coding language. Similarly with data transformation the emergence of frameworks such as DBT has massively decreased the reliance on third party software tools.

14. Do your BI tools enable business users to investigate data on the fly?

In addition to analytics teams, many organisations now have business users who are comfortable with data and are happy to perform minor investigations into data. There are several tools that enable users to do this (Tableau, Looker, PowerBI) as well as serving the analytics team and hosting business reporting.

These tools enable the whole business to make data-driven decisions and ensure data is being effectively used.

People & Organisation

15. Does your organisation provide an attractive career for analytics talent?

Data analysts are highly sought after in today’s business environment, and for good reason. Data analytics, if used properly, has the power to transform your decision making and therefore the business. The best analytics talent wants to work on interesting and important business problems and wants to work with the best technology.

16. Are there no significant skill gaps within the analytics professionals in your organisation?

A data analyst requires a range of skills to be effective. These are not just technical skills like programming or statistics, analysts must have the ability to apply these skills to real business problems. Additionally, analysts must be able to communicate complex ideas and insights to non-technical business users. Without the correct mix of these skills, it is extremely difficult to effectively use customer analytics in your organisation.

Asking these questions of your organisation

We’ve outlined how these questions can help you determine your level of sophistication in customer analytics. They should give you a very clear idea of the gaps in your organisation and why it is important to plug those gaps.

If you would like to discuss how Conjura can help you improve your level of sophistication with customer analytics, please don’t hesitate to reach out to foresight@conjura.com.

Building Your Data Architecture vs. Your Skincare Routine

Much like flawless skin, insightful data is not a given.

While some are just seemingly blessed with luck of the draw DNA, the vast majority of us must put planning and processes into place in order to achieve the glowing results of our dreams.

We’ve all walked into a department store beauty section and needed a minute to steady ourselves amidst the dizzying display of lotions and potions promising to fix facades and give meaning to our lives. With so many options and opinions, it can be hard to know where to even begin with your skin.

Building your business’ information architecture can feel similarly discombobulating and with such high expectations on the results, you may be tempted to grab a handful of solutions and get the hell out of dodge.

But never fear! Because whether you’re building your skincare routine or carefully crafting your data architecture, there are tried and tested tips you can follow to get you from here to the place you’ve always wanted to be.

So let’s think of your data architecture as consisting of three main steps: The thinking, the doing and the maintenance. Allow us to break it down for you:

To start off your journey, you’ll need to make a plan before you can stick to it. Which means mapping out where you are now vs. where you want to go. So sit down, take a deep breath and think about what you want. Once you have clean and clear access to your data…what do you actually want to do with it?

When it comes to your face, the likelihood is that you came to the game with a pre-existing laundry list of symptoms to sort out on your journey to achieving skin Nirvana. Redness, dryness and discolouration to name a few. And while your data needs may appear murkier than your complexion – we promise that enlightenment is in your future.

To help you get that clarity start with Tip #1: Involve the end users of your data in the planning process.

You wouldn’t expect a dermatologist to prescribe a skincare regimen without having both spoken to you about your goals and seen your skin in the flesh. That would be ridiculous wouldn’t it. We’re glad you see that! But believe it or not, one of the most common tragedies to befall businesses when building their data architecture, is allowing leadership teams who have no involvement in using the end data, to call all the shots.

Take this approach and all that blood, sweat and tears will be for nothing when you overrun on time and budget; ending up with a solution that nobody actually uses. Because despite the pretty packaging and your shiny enthusiasm for a much needed change – one solution does not suit all.

So speak to the end users of your data and decide what basics you need in your arsenal: A thick layer of analytics to track your chosen metrics and a spritz of reporting for greater visibility on your overall business performance. Then finally, what data models are going to provide you with the necessary insight to take your business from downright dull to dewy.

Now that we know where to begin and who to trust, let’s delve into Tip #2: Don’t over complicate your build.

So you want it all? An all singing, all dancing solution that promises a future of untold data delights. Listen, we get it. We really really get it. The temptation to throw one thousand new products at the problem and hope that one one of them sticks is understandable. We all want in on the latest and greatest super-ingredients for success, but do we really need to use all of them? Probably not.

If you did this with your skin, you would have a pretty poor sense of what was working for you and what actually wasn’t. This is a great way to spend a lot of money with very little measurable ROI to show for it.

Instead consult with your data engineering and analytics teams to decide which tools are suitable for your business’ unique landscape and volume of data. Choosing the right tools will help everyone to maintain faith in what your business is trying to tell you and it will be far easier to get sign off to turn your insights into action.

So now that you’ve thought long and hard about your needs and assembled a council of wise folk to steer you well, it’s time to begin. First, decide which sources of data are valuable to your business. These could be your google analytics, your shopify account and your ERP system amongst others. The basic premise of your data architecture is to afford you a deeper view of your business by funneling all of your disparate data sources together into one easy breezy beautiful data hub.

This is the only way to truly get your story straight.

Then, much like with your precious visage, you will need to get your data cleaned, cleared and under control before you can hope for any bigger transformations. It’s important to treat the skin you’re in, so you’ll need to cleanse and standardise before you can make accurate assumptions about what is working for you and what isn’t living up to the hype.

Which brings us to Tip #3: Stick to the steps.

Just as with your carefully assembled skincare routine, your information architecture must also be deployed in the correct order. A modular approach if you will. For example, those in the know wouldn’t dream of applying a thick product before a thin product as the former would reduce the effectiveness of the latter.

This would be like throwing money down the drain. So if you thought you could save some time by skimping on your cleansing and jumping straight into some fancy data modelling – think again. This approach would have you continuing to report on siloed information instead of gaining a greater understanding of your business by cross referencing multiple data sets.

So you’re following all the rules. What now? How about a reminder that patience is a virtue with Tip #4: Give it Time.

There is no such thing as an instant fix. Whether you’re looking at your skin or your data, real results are only seen through consistency. So if you’re prone to binning products that don’t yield transformations overnight, you are likely to be missing out on some radiant results in the long run. Remember that your information architecture needs time to actually accumulate historical data in order to make any meaningful connections and generate those informed insights you so desperately want and need.

Don’t sit around and pick holes in the process. Instead, keep sitting pretty for a minimum of 6 months after your data models have been put into place in order to get a real read on where you’re at. Occasionally after this amount of time you might even uncover gaps in your original knowledge that will require you to reassess your system and possibly rejig, recleanse and rebuild. If waiting that long seems like cruel and unusual punishment – just remember that sometimes beauty is pain.

To maintain or not to maintain? This should not be the question. Why do we seem to think that the moment we’re finally looking fresh-faced and blemish free, that it’s a sign from the universe to sit back, relax and let our cards fall where they may? For those of you who believe that your work is done at this stage – we have some terrible news. Your skin is subject to change. It will age, it will be affected by your lifestyle, environment and daily activities. Sometimes it will bend or break it ways you never foresaw. The way you care for your skin will evolve throughout your life. The way you care for your data is no different.

With that in mind our last piece of advice is of the utmost importance. Tip #5: Build trust in the data.

Do you remember that one time when work was hectic and your zoom socialising got a bit out of control? You were tired, you were busy and you started slacking on your skincare. Suddenly you were adding breakouts and breakdowns to your weekly to do list and cursing your face for not staying in its place. But the truth is that your skin is a living organ and needs to be monitored and maintained to function at optimal capacity. So too does your data.

Even the most meticulously planned information architecture can crumble if not given proper attention – and in reality if your business doesn’t trust the data, there is likely to be little use of the analytics and reporting you so painstakingly put in place. Inevitably things will go wrong, but if you stay primed and ready to address any issues that arise, then you won’t have to waste time and money finagling resources to save you from becoming just another data school dropout.

Remember that if your team is in doubt, they’ll leave it out. So be sure to dedicate regular TLC to keeping your data architecture alive and thriving. In reality your journey with data shouldn't be a million miles away from the care and consideration you already give to your skin – either way you deserve a solution that is bespoke to your specific needs and challenges. Because at the end of the day, you’re worth it.