The value of the data an e-commerce business holds will become even greater once Google phases out tracking cookies from Chrome next year. However, it’s only valuable if that business can extract and act on data insights. Consequently, business owners and leadership teams need to understand and use the data they sit on from day one – it’s non-negotiable.
There are significant funding opportunities in e-commerce at the moment. It’s not a free lunch though. Any investor worth his or her salt will be benchmarking your processes against your peers, both in terms of current and potential performance. An investor will simply walk away if there’s no data analytics process or strategy in place, the same is true if they don’t like what they see during the due diligence process.
Previously, due diligence ahead of an investment or acquisition was conducted around four pillars – legal, operational, commercial and technical. The data for this was provided in advance by the business under scrutiny and held in a data room – a rather grandiose term for a secure online digital locker. However, the data was not ‘live’ and most analysis was completed manually.
That’s changing and digital due diligence can now be automated, anonymised and completed in (near) real time by plugging in to the company’s ERP and other related systems. Working up from an individual product per transaction level of detail will help identify the growth opportunity – and potentially uncover unrealised avenues.
On the other hand, there’s nowhere to hide. So, before any e-commerce business even considers putting itself forward to a potential investor, it needs to get its data story straight.
In the current landscape, the investor will be working on the assumption that the application of data is a given. The baseline for this is the company is using data in a way that is demonstrably driving revenue growth.
The amount of value a business can extract from its data through actionable insights reveals at what stage it is at on its data transformation story. This can be broken down into five stages, from chaotic through to intelligent. Hold off on preparing those investment notes until you’re at least mid-way through this process.
In practical terms, this means being able to demonstrate you are analysing and making commercial decisions based on the right data. If you are unable to provide the specific data an investor needs, they are essentially flying blind and it’s the end of the conversation.
So, conduct some due diligence of your own on your data best practice to ensure you won’t fall foul of any investor red flags:
The data you need to fulfil your stated growth strategy should be held in a database that, at the very least, offers the potential to feed into the broader marketing and sales suite to be accessed across the organisation.
That data should be ‘cleaned’ and de-duplicated so your analytics tools can extract the value. You also need to have a dedicated (cross-departmental) data analyst in place, your data is too precious to be foisted just on the marketing, or the IT team.
The end goal for an intelligent data business is an integrated data architecture, which would enable automated reporting. You don’t need to have this in place to talk to an investor, but you should clearly have the foundations in place.
There’s little point in collecting data for the sake of it. Data strategies require you to track and act upon data that will enable you to meet strategic objectives. These might include increasing the frequency of sales to existing customers, or reaching new audiences. If you’re wasting time analysing everything, the investor will realise you aren’t strategic, or you simply don’t ‘get’ data.
the devil really is in the detail. Investors want to see granular analysis of unit economics that will reveal competitive advantages and growth opportunities. This relies on regular monitoring, you should be collecting and analysing data on a daily, or at the bare minimum, weekly basis.
Key metrics for review include: customer acquisition costs as broken down by marketing channels; the value of customers – by cohorts and by geographies; what products they buy; on what device – and so on.
In particular, e-commerce businesses should aspire to grow profitably through the long-term value of their customers. It is possible to throw money into acquiring new ones, but if they don’t keep buying it offers a poor return on investment. If the cost of acquisition is more than the lifetime value over a specified time period, this demonstrates not only that you don’t understand the data, it also reveals poor leadership full-stop.
Investors always look first for reasons not to invest. Demonstrating you truly understand what your data is telling you will be the defining factor between a successful application and a failed one. Any investor needs to be confident you know what you’re doing. That means having the capacity to surface the insights that demonstrate clear growth potential and the business nous to interpret them and make informed decisions.