What’s the hardest role in retail? You could make a great case for jobs across the board – the Chief Executive, the store manager, the buyer, the shop assistant.
For us, though, the most difficult role belongs to the Chief Customer Officer (CCO) – especially in the times we’re currently living in. The pandemic played havoc with customer spending and now the cost of living is rapidly rising, so customers have less disposable income. Supply chain disruption is still making it hard to get products on the shelf. And customer expectations have gone through the roof, largely driven by companies like Amazon and Google.
Great decisions = high business performance.
Survival in retail is determined by a handful of basic, but critical, decisions. What products should you sell? Who should you sell them to? How should you sell them? What price should you charge? A CCO is responsible for anything customer-related – including these decisions.
Successful retailers make quality commercial decisions in each of these areas. This isn’t just a hunch – Bain’s 10-year study found a clear correlation between decision effectiveness and performance.
Major barriers stand in the way.
So, what’s required to make great commercial decisions?
Most importantly, retailers need to be able to analyse data at scale to produce actionable insight. For example, a grocery retailer may uncover the behaviour of a particular segment of shoppers. By understanding what’s driving that behaviour, they can tailor their product offer to appeal to that segment, then price it in a way that converts more browsers to buyers.
Sounds easy enough, right? Unfortunately, major barriers stand in the way.
Analysing data at scale typically requires the ability to code and extract insight from data. But there’s a chronic shortage of data science and AI talent globally, and the gap is widening.
One way of mitigating the talent shortage is via affordable, accessible tech products. But few truly no-code products exist today.
Enter AI + BI.
Artificial Intelligence (AI) is reshaping the world we live in – including retail. On the flip side, Business Intelligence (BI) is a field that’s been around some 20 or 30 years. Together, they create a compelling proposition for businesses – including retailers and brands.
AI helps retailers optimise commercial decisions through data and insight. It’s tempting to claim it’s the answer to multiple challenges in retail and beyond – but that’s only half the story.
AI tends to be most successful when applied to repetitive scenarios with lots of data points driving decisions. Take supply chain decisions, for example. Which products need to be on the shelf? When should stock be reordered to maximise availability, whilst simultaneously maintaining warehouse efficiency? How much stock are we selling? When do we expect to sell through based on current demand? These decisions can be solved using AI – for instance, intelligent markdown and fulfilment.
Let’s revisit the key decisions faced by a CCO in retail, coupled with the AI tools created by data scientists to help:
What products to sell.
Assortment optimisation algorithms balance customer and commercial objectives. Meanwhile, recommendation engines serve online customers with curated assortments.
Who to sell them to.
AI can help create a rich understanding of a customer base, including their loyalty and needs. It can also help provide relevant customer experiences using segmentation, customer lifetime value and intelligent audiences.
How to sell them.
AI can help inform us of a customer’s channel and communication preference, or what promotional mechanisms are really effective.
What price to charge.
‘Share of choice’ models help retailers understand themselves vs their competition. Meanwhile, price elasticity ascertains which areas they can increase pieces safely in today’s climate.
However, despite the growing use of these tools, AI alone isn’t enough to make smart decisions. The barriers we typically see are:
- It often exists in silos – only serving a single use case, when other areas in a retail business could benefit.
- It’s often a one-off exercise – sat outside the production environment, gathering dust and aging rapidly.
- It’s often poorly understood by the people who could benefit from it – AKA the actual decision makers in a business.
- It requires intervention when unexpected macro events happen (such as the death of the Queen).
AI is great, but there’s a key component missing (spoiler: it’s not just BI).
Over the last decade, it’s become the status quo to have more BI than you can shake a stick at – across almost every industry, not just retail.
BI is a fantastic tool. It’s not hard to see how great reports, focused on action, can help a CCO make lots of good decisions around what products to sell and who to sell them to.
However, BI alone is not enough to win in today’s fast paced world. And there’s a few reasons for that:
- BI tends to be focused on providing decision makers with a record of what has happened… rather than moving into the territory of prescriptive analytics or recommendations. Those things are smarter and come from AI tools.
- Most BI is produced for BI’s sake and along the way we’ve lost the key focus – making smarter decisions. Less is more when it comes to information for decision making, not the unlimited ability to slice and dice every metric ad infinitum.
- You can’t take action in a BI platform – for example by selecting audiences for campaigns or deciding which products need to change price this week. This barrier slows down decision makers.
Clearly then, AI and BI as discrete business tools aren’t perfect. But there’s hope!
We think we’ve cracked the code for the Chief Customer Officer and other decision makers in an equation Pythagoras would be proud of.
AI + BI = DI.
But what exactly is DI? DI – AKA decision intelligence – is a rapidly scaling category of analytics and decision science that brings together the best elements of AI and BI. As the name suggests, its sole focus is to help make intelligent decisions simple and seamless:
- It productionises and embeds AI into BI-like tools. This allows decision makers to view key metrics, enhanced by AI outputs like segmentation/customer lifetime value, and receive next best action recommendations with a forecast of likely outcomes.
- The platform or tool should give decision makers the confidence to take action. It should provide live connectivity to downstream platforms to implement those actions.
Decision intelligence makes the complex simple.
Crucially, though, decision intelligence supports humans with decision making. Let’s face it, humans don’t like being told what to do – especially when they’ve garnered years of subject matter knowledge and experience. Instead, the data directs, the human decides, then a human or machine deploys the outcome.
Make intelligent decisions. Simple.
HyperFinity exists to embed AI and BI into retailers’ decision-making processes.
Our solution is Powered by Snowflake and acts as an intelligence layer for a retailer:
- Connect two simple data feeds.
- Create foundational data assets, such as product attributes, customer needs states, customer segments and lifetime value. This insight unifies your understanding of products and customers. HyperFinity stands up this array of intelligence faster than any tool on the market – and faster than internal data science teams.
- Deploy those foundational data assets into easy-to-use front-end tools focused on making certain decisions intelligently – across marketing and media, pricing, assortment optimisation and supply chain.
We want to democratise access to data science and AI and embed decision intelligence tools into retail decision makers’ day-to-day lives.
We think we’re on to something here.
We’re dramatically lowering the bar for retailers and CCOs to make great commercial decisions, informed by data. And Snowflake validated that belief by naming us one of the top three start-ups globally building on their data platform earlier this year.
Retailers are facing serious headwinds. Decision intelligence truly offers an opportunity to optimise commercial decision making and deliver customer experiences which generate the revenue and profit needed to survive the storm and thrive in the future.