Guest post – Selling on Insight: How to overcome barriers to consumer behaviour

As retailers and brands question how best to get under the skin of consumers, what’s the answer? Retailers’ journey to truly understanding their customers is both a technical and cultural one, says Sarah Hughes, Co-founder & Director at Datitude.

Most retailers claim they understand their customers.  Yet when they try to bring all their intelligence together to manage multiple communications across multiple channels, they will be aware of a rapidly widening chasm between what they can, and need, to do.

Sarah Hughes, Cofounder and Director, Datitude

Consider the sheer breadth of what the customer wants.  In fact, it’s what they’ve always wanted, but they are increasingly voting with their wallets if their demands are not met.  They now expect immediate, responsive and consistent pricing, service and product descriptions across every device and channel, as well as demanding the best prices and the most generous promotions.  Consumers expect products to be in stock, and with accurate stock information displayed and available to them.  They want flexible, and convenient, delivery and return options (free even) and, more so than ever, they want retailers to show they have deep social and environmental commitments, including ethical labour and supplier management practices. The myriad of changing demands goes on.

However, few retailers have bridged this gulf on their journey to greater customer-centricity, and yet, there is a huge opportunity for retailers to close the gap between perception and reality, and between inability and capability to execute.

Retailers have known for some years now that their data is the backbone of this opportunity, but even now, most still struggle to consolidate this data to extract meaningful value from it and deliver the results needed.

Why is it so difficult to understand customer behaviour?

The problem is essentially a technical one, but it has a cultural impact that is perhaps harder to fix, as we explore in our latest report, ‘A Little Data Is A Dangerous Thing’.  The technical problem sounds simple enough; as if its simplicity was somehow no excuse for it being a problem at all.  Data tools are often separated from the data repository, so retailers don’t have a single view of the truth of customer status, behaviour, or preferences, all of which are critical to providing a personalised service and increasing value.

Working within these limitations, retailers try to manage disparate data from multiple systems – finance, POS and/or eCommerce, web analytics, social commerce, merchandising, and then try to extract value using many different point solutions. This almost always requires laborious extracts of data from each system into Excel, with complicated formulae to bring it all together, often resulting in inaccurate assumptions or complex and meaningless insights that add little value to the retailer or, indeed, the customer.

To add to this, the data is often useless because it took so long to extract in the first place. Like-for-like reporting history may depend on saved versions of Excel reports, making new analysis of older data tricky.  Moreover, point Excel-based solutions are notoriously difficult to validate, and keep validated, and are highly dependent on the original author.  Attempts to resolve this results in people rushing around fixing data flows, re-running batch processes, and validating data that should be inherently trusted.

It lacks depth and reveals only the truth of its own narrow limitations rather than insight into the KPIs that drive modern retail; segmentation data, lifetime value, acquisition, reactivation, retention, as well as commercial data on sales, returns, and profit and loss.

As a result, retailers can’t take advantage of new opportunities or ensure a profitable return on investment.  They miss out on seeing the correlation between, and the impact of, marketing decisions on customer acquisition, engagement and retention, and knowing the true cost of investment decisions.  Such data insights are critical to stop retailers wasting time, money and resources on poor choices and start making decisions which optimise ROI with greater confidence.

The cultural problem stems from this partial view of data and is often coupled with “not invented here” or “let’s reinvent the wheel” syndrome.  It can affect any part of the business that is driven by raw results and partial views of data that serve only their own ends, rather than the business’ wider turnover and profit goals. Viewed from a shallow angle, everything may look and feel ok and departmental performance may even be rewarded with further investment.  This way of operating is superficial as it obscures the bigger picture; one of profitable growth and a more customer-centric approach based on building loyalty and customer lifetime value.

So how do you overcome these barriers to understanding customer behaviour?

The answer is an integrated data lake and business intelligence (BI) solution that can provide perfect real-time, on-demand, insight into stock, customers, sales and returns.  One that enables advanced data-driven analyses that are so essential to modern retail in driving personalisation, cross-channel marketing campaigns, product bundling, dynamic product lifecycle management, efficient customer reacquisition and channel optimisation, to name a few.

The decision to change generally needs to be taken at C-level because this is the best way to confront both the technical and cultural barriers.  However, implementation, and adoption, should be much easier than many people anticipate.  Retailers can continue to use their existing BI and reporting tools because now they will be accessing pre-validated data that’s guaranteed to give all departments the same view of the truth.

And multiple data models should already be built in so retailers can run straight from the box to get immediate value, and then build more sophisticated models over time.  This is important as the sheer range and sources of data have and will continue to expand.  Take zero and 1st party data for instance.  It has become increasingly important, partly due to the demise of 3rd party cookies, for understanding consumer behaviour, retaining customers and building loyalty, rather than just trying to acquire new ones.  After all, isn’t this a key driver for profitable growth?

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