28 March 2017
It’s no secret that success in retail today requires the ability to leverage customer science and data strategically and use those assets to turn insights into rapid action. To survive in this very competitive industry and retain or grow market share, retailers are being forced to continuously move with the times, adapting their offer to reflect changing consumer behaviour and ensuring their customer experience stacks up against that of their competitors – both online and in-store.
Being an industry which is incredibly rich in data, as a collective whole, retail has obviously grasped that it cannot be left behind in the race to understand customers better – with predicted expenditure of USD $5.1 Billion on retail analytics by 2020 (an increase of 18.9% in just 5 years).
Committing significant investment to make better use of their customer data is certainly high on the list of priorities of many retailers around the world. A recent Forrester survey found that 69% stated that “utilising customer data effectively was a critical challenge for their organisation” and was “impeding their ability to develop and execute customer-centric strategies”. The need to prove demonstrable return on their analytics investment will be the next thing keeping retailers awake at night.
With the proliferation of different data sources growing all the time, particularly with customers increasingly living much of their lives online now, retailers need to think strategically about which data sources can combine to create insights that will drive differentiation. Key to success is focussing on practical applications which will improve day to day decision making and performance across the store.
Here are some examples of what next-gen customer science is helping retailers achieve:
Moving from mass and segmented to personalised 121 communications and content
Segmenting customers based on buying habits and reaching out to them with relevant messaging at the right time has been superseded by individualised, real time content and offers, using simulation modelling to predict customer behaviour alongside historic behavioural data.
Joining up behavioural with attitudinal to get a 360 degree view.
Social listening, sentiment tracking and customer service feedback – these are just some of the sources of unstructured data which can now be analysed through algorithms which allow natural language processing. Combining structured and unstructured data enables richer insights on what customers are thinking and feeling, not just what they bought and how much they paid for it.
Optimising price and making promotions profitable.
Having your pricing and promotions market-led rather than customer-led, in a race to remain competitive, leads to ever-further erosion of already slim-margins in grocery retail. Chasing short-term uplift from over-promoting leads to high cannibalisation, low or no volume growth, and degradation of your brand. Price and promotions play a key role in the “surprise and delight” aspect of the customer experience, so having the right science to effectively assess which promotions should be dropped or kept, and which products don’t require a competitive price point, can make an enormous difference to the bottom line.
Smarter assortment and greater efficiency.
A bloated inventory is bad for both customers and retailer, leading to operational complexity in the supply chain, availability issues and complexity in the customer offer. Time-pressured customers crave simplicity and leaner ranges with the right breadth and depth of products can help deliver a better shopping experience and streamline operational efficiency for the retailer. Traditional product rationalisation techniques risk removing unique products which may be low sellers, but are key lines for high-value customer groups. Sophisticated customer behavioural analysis is helping retailers weed out products which are not customer-centric and contribute little to the shopping experience.
Action based on insight requires clever analytics and creative ways to leverage new forms of customer data. The question for retailers is not whether to invest, but how to ensure that investment will access the opportunity from your data asset in the right way.