The phrase ‘data-driven decisions’ has become a fact of life in the corporate world, regardless of industry. It is for this reason that Elixirr’s own acquisition last year of iOLAP,  a data and technology consultancy, has been so important and beneficial for our clients. However, there is a lesser-discussed side of this increasing focus on data: as it becomes increasingly easier and cheaper to collect due to the growth of cloud storage solutions, many organisations are unable to invest the time and resources necessary to get the value out of data so it can be used effectively by the right user at the right time. It costs money to clean data, organise data, protect data and analyse data before it even reaches the point of informing business decisions. While this is recognised as a core capability for corporates, where does it leave start-ups with tightly managed cash flows? Particularly those in the retail and consumer goods sector, where the offline and anonymous nature of sales presents an extra data challenge.

Using our global innovation network of VCs and startups, we work with clients to run immersion sessions and set up partnerships to solve for their challenges with the most innovative market offerings. And through this experience, we have seen the differences in data collection, analysis and usage between corporates and start-ups, which can cause friction in partnerships. We have outlined three key learnings on bridging the gap between corporate expectations on data and the reality for startups. 

1. Corporates must understand the data challenge startups face and manage expectations accordingly

Startups generally have access to less data than corporates. It may be the case that, in comparison to legacy organisations, startups are far better at utilising the data they do have on their own customers; however, competitor and third-party data is expensive and creates an obstacle for startups to understand their market and channels. The retail industry has traditionally lagged on customer data given the lack of customer identification and online footprint – it was for this exact reason the Tesco Clubcard was so revolutionary when it was introduced nearly 30 years ago.

For consumer goods startups, it is difficult to even know how many of their products have been sold. While working with the corporate venture capital arm of a multinational consumer goods conglomerate, the corporate commercial team requested monthly reports from their startup portfolio. They were surprised when a Canadian confectionary startup highlighted that the only way to estimate their point-of-sale data was if their distributor provided data on how often stores were reordering stock, which they only did on an ad-hoc request basis. There are obvious flaws in this data collection method, such as inconsistency and the fact that the product may have been out-of-stock for weeks before stores reorder. It also provides no benchmark for performance against competitors, but the cost of a one-off NielsenIQ market report – CAN$3,800 (~£2,400) when they enquired – was prohibitive and the startup decided the budget would be more valuably spent on marketing to drive customer acquisition.

Startups must not only weigh up their appetite for spend against the value of the dataset, but also the team capacity and capability to effectively evaluate the dataset to extract actionable insights. At Elixirr, we are firm believers in “the team you have is the team you need.” In the example outlined above, our client, which had platinum access to multiple retail data platforms, had to learn to change their mindset from expecting regular reporting before making decisions, to accepting whatever information they could get and progressing with other sales activities in the meantime. In this case, the buyer for one of the convenience store chains provided six-weeks’ worth of point-of-sale data as a favour with the comment “we would class 80% of this figure as excellent” as a benchmark.

2. Corporates can benefit from adopting elements of the startup approach to data

Lack of available data drives startups to narrow their focus to what they do know. During a conversation with a well-known peer-to-peer circular fashion company, they told us that their research analysts are working at full capacity and teams are often competing to get their data requests prioritised. So, the web product manager had to take it back to the basics. The key KPI for the quarter was improving conversion rate, so they logically concluded that they needed to focus on the ‘shop’ pages as this is where buying decisions are made. They got their team to run A/B testing with different layouts on this page and the results are looking very promising, with the leading design generating a significant increase in conversion rate.

This outcome-focused approach is most famously seen in ‘The Amazon method‘. Before pitching a new product, Amazon product managers must write an internal press release announcing the product, outlining the customer problem, how current solutions fail and how this new product will outperform these existing solutions. Applying this to corporate data analysis, organisations can often get overwhelmed with the high volume of data available and forecasting the impact of each decision. One study found that only 40% of consumer goods companies that have made digital and analytics investments are achieving returns above the cost of capital. Instead, companies must prioritise the end goal, then work backwards to decide the data points required to achieve this. If the data is not available, a decision must be made as to the cost of acquiring the data versus experimenting to both fail fast but also learn fast. It comes back to the reason many of our clients partner with startups in the first place; agility and flexibility are critical to winning business and innovating to stay ahead of disruption.

In an environment where consumers and governing bodies alike are increasingly aware of data privacy and personal data protection, even industries that have traditionally had greater access to consumer data, such as ecommerce companies, are facing data availability challenges. It was this requirement for avoiding privacy concerns that inspired Elixirr’s data team to propose an innovative solution for a quick-serve restaurant client. The restaurant chain wanted to analyse the end-to-end order-to-pay journey from joining the queue to consumers receiving their food. The iOLAP team offered the solution of using AI to track customers through the process, but rather than using facial recognition (which would raise data privacy challenges), the system would recognise people’s shoes to gather data on the time taken to complete the journey and identify bottlenecks. This sort of ‘hacky’ thinking is more often associated with startups, but there is increasingly room for it across all types of organisations.

3. Corporates and startups alike can capitalise on innovative methods for data gathering

Gathering your own data is time-consuming and logistically difficult; purchasing datasets from aggregators is expensive and becomes even more expensive if it needs to be bespoke to your requirements. So how can new tech support corporates and startups alike in capturing the data they need? Perhaps unsurprisingly, the rapid improvement and prevalence in artificial intelligence comes into play here.

We are seeing the emergence of companies using AI to offer image recognition to improve the efficiency of CPG sales teams, such as ParallelDots, which completed a Series A funding round with one of our clients last year. Our own data team, iOLAP, have been working with a US sunglasses manufacturer to analyse videos of display stands taken by regional sales reps. The initial solution will remove the need to manually complete tedious inventories. The ultimate vision is that, once completing the inventory, the solution analyses multiple retail and consumer data sources to make recommendations for which products are likely to sell best in this location and even the shelf positioning these should be displayed in.

While this approach helps companies with existing sales reps, how can it be adapted for startups where the entire team may only be a handful of people? A new key term comes into play here: ‘crowd empowerment’ – the practice of tapping into the collective insight of millions of individuals, their communities and organisations in order to output shared knowledge. Data collection moves away from being a one-way street to rewarding customers directly for providing their data points. These rewards are usually financial – such as YouGov Finance paying users in points (which can be exchanged for cash) for connecting their bank accounts with open banking. This means their transaction data can be collected and analysed to uncover market insights and consumer behaviours trends, but the rewards are still much more economical than expensive professional surveys. The key benefit touted by providers such as Premise is the fact that companies can be very specific in the insight they require and get real-time information from across their global community (spanning 140 countries) so that they have the agility and flexibility to make adjustments as the study is underway and can begin actioning insights immediately. By engaging consumers and potential consumers directly, corporates and startups alike can see what they see and hear what they think, so they can truly understand their consumer base, which is the key to growing it.

A change of mindset

In our experience, the biggest blocker companies face when tackling data is mindset: they believe that they do not have the technology, team capability, or foundational processes and understanding to solve this complex challenge and utilise data beyond operational reporting. To this, we always say “the data you already have has a tonne of value and the team you have is the team you need”. You do not need a perfect data repository, advanced analytics expertise, or a large tech team to jump up the data maturity curve and begin building towards an excellent self-service data platform. Whether your organisation has more data than it knows what to do with or is just starting on its data journey, our recommended core steps are to recognise what data you have, be clear on what information you need and then decide how you want to get to it – through rapid testing, failing fast and learning, or through tapping into the innovative collection options that readily-available tech is able to offer. Our advice is to not let the focus on making data-driven decisions stop you from making the decisions you need.

At Elixirr, we have a wealth of experience working with organisations of every size to determine the information they need, gather the data to provide it and establish seamless methods to extract the insights required to make smart business decisions. For more information, or to kickstart the conversation about your organisation, get in touch with our data experts today:

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