More Grocers Need to Leverage Big Data Analytics

By | May 28, 2015

Grocer loyalty program databases, rich with detailed customer data, have been in existence for decades, providing grocers with an apparent advantage (for those that have exploited this data) compared to other retailers. Grocers have had a head start on leveraging this data in better understanding consumer behavior and shopping preferences.

However, with the advent of new technologies, new competitors, new channels and the emergence of an ‘always on’ and ‘time starved’ consumer base with a myriad of convenient shopping options – the grocery industry is now, surprisingly, lagging other retailers in the ability to leverage these new ‘big’ data sources to evolve their analytic capabilities from transactions to interactions. In particular, the emergence of new data types – big data – provides an opportunity for small to medium-size grocers to compete better with larger chains.

Big Data is available to small and mid-sized grocers: it’s about leveraging all the data.

At the end of the day, ‘big’ data is just data – but today there are more data sources than ever. Stepping back from the hype for a minute, what it really comes down to for grocers is leveraging all of their data through analytics that enable differentiation in a marketplace. And it is a crowded marketplace that continues to see growth in brick and mortar specialty food retailing and expansion of grocery sections in other retail formats. This includes a growing selection of food products at online retailers and virtual grocery store fronts providing a myriad of pick-up and delivery options, even intraday fulfillment capabilities.

In terms of big data, grocers in general can improve at leveraging the data they already have; there are many more marketing and selling opportunities to be realized! For example, transaction data is still the cornerstone for grocers – and there is still untapped analytic potential. In a sense, it’s ‘big’ data from a volume perspective, and grocers of every size have effectively ‘run the business’ well beyond basic sales reporting using transaction details for basket and affinity analysis. Opportunities include leveraging advanced analytics for demand forecasting, promotional planning, basket segmentation, price elasticity modeling, assortment planning and so on – this still remains the lifeblood of operational improvement for grocers today.

Also, customer data combined with transaction data enables grocers to better understand changing customer behavior, preferences and response (e.g. segmentation, propensity to buy, attrition) to more effectively target promotions, localize assortments and drive efficiencies into overall marketing spends and operational tactics. Most grocers have had ‘loyalty’ programs in place for decades, but even in absence of a loyalty program, others have been very creative in their quest to obtain customer data, such as Walmart’s Savings Catcher program.

In terms of big data, the broader retailer industry is moving beyond the ‘brick and mortar’ environments and looking at integrating data – and marketing – across channels. We are seeing lots of activity – in IT investment and analytic discovery. This is where the trends in ‘big’ data tend to focus; it’s on mining new levels of data complexity and velocity (e.g. social, mobile, text, sensors). Yet there are great opportunities to explore in this space for grocers of all sizes, and there is no ‘right or wrong’ place to start.

Some examples of opportunity:
o Web log data from their own website to better understand consumer preferences and interests (e.g. shopping lists, recipes, advertised items, special events) and cross-channel interactions (e.g. clip online, redeem / or not redeem in-store). Being able to improve customer experiences by providing real-time product recommendations, improving search results, and personalizing events, rewards, front page features and optimized discounts is where the rest of the retail industry is already investing heavily – just to keep up with the likes of Amazon.
o Location aware data (mobile, in-store beacons) to better understand purchase paths and enable ‘real time’ interactions while consumers in your store, in a specific department or even in front of a specific category – beacon are a relatively inexpensive investment that could provide a very quick return on investment for even a single store operator.
o Vehicle sensors and OBC data to improve fresh product shelf life, reduce vehicle maintenance costs, reduce fuel consumption, improve network optimization and supply chain visibility are just a few – reducing costs in any of these areas by just a small percentage could be worth hundreds of thousands of dollars to any self-distributing grocer.
o Text-based data converted into digital form to better understand operational improvement opportunities (dirty bathrooms, out of stocks, etc.) and improve consumer insights (beyond transactions) by integrating consumer surveys and call center logs – the ability to respond in days versus weeks can have a direct impact to the bottom line for grocers of any size.
o Social media data (e.g. Yelp!, Facebook, Google) can be powerful brand builders and sometimes PR challenges. For businesses of any size, monitoring consumer sentiment is no longer a luxury – it’s a space to cultivate. As a strategic business tool, they can also provide grocers with a glimpse into how regional and even specific locations stack up against the competition.

Are grocers dealing effectively with Big Data? Making it a true business advantage?

Most grocers have been leveraging transaction and customer data for years, even though many have yet to fully exploit all that can be accomplished with just this data. For small to mid-sized grocers, many have turned to outsourced point solutions due to lack of available analytics resources and potential infrastructure investments needed. The problem with point solutions is just that – they individually solve for a specific business area and the analysis is cookie cutter. Therefore, the ‘data’ is not integrated and difficult if not impossible to provide a holistic picture of customer behavior across all touch points (for example), nor are the analyses providing a cross-functional view that is relevant to all business stakeholders in terms of driving differentiation in the marketplace in merchandising, marketing, supply chain and store operations.

In terms of leveraging ‘new’ data sources such as social, mobile and text, the industry is very much engaged in a ‘discovery’ stage of analysis with a variety of focus areas, testing and learning how to extract value from these rich new sources of data. There are two typical paths grocers are taking regardless of the ‘size’ of the organization. First is Strategic Commitment, in which there is C-level (organizational) commitment to making the investment in the resources to bring all of this data in-house and analyze it.

Now more than ever, data, analytics and IP are viewed as strategic assets and competitive differentiators. The other is Business Discovery, in which grocers outsource to an Analytics as a Service firm to leverage internal and external data. Conducting analytic sprints to build the business benefits can result in new use cases and help capture ‘quick wins’ before making the resource commitment to technology and human capital up front. Based upon success, and a smart, trusted partner willing to share the methodologies and analytic models, grocers can continue on with an outsourced services provider or migrate the data, analytics and IP in-house.

Both of these approaches have been under way within the mid- to large- size grocers for several years, potentially putting those standing on the sidelines at a severe strategic disadvantage as the entire grocery industry is playing catch-up to the general merchandise retailers that have had to react much quicker to the threat of online retail – several of whom paid the ultimate price of obsolescence along the way.
We are nonetheless busy working with grocers that are dealing effectively with Big Data.

My colleagues and I – in the Retail Business Analytics Consulting practice – work very closely in providing advanced analytics / data science services with many of the leaders (of all sizes) across the grocery industry. There is certainly no shortage of success stories. Without sharing sensitive business details, we can tell you that many of our grocery clients, such as Walmart, Whole Foods, HEB and Publix in the US and METRO Group, Tesco and Sainsbury’s in Europe – all are actively involved in big data transformation.

Yet as engaged as we are in serving grocery customers, the opportunities are enormous – and we are excited to be helping many grocers become more data-driven – as they will need to be to flourish in the future. – By Brent Buttolph

Brent buttolph best bio pic

Brent Buttolph, Principal Consultant, has worked with leading retail corporations to provide thought leadership and align strategic objectives with analytic capabilities to create a differentiated competitive advantage in the marketplace. Brent is responsible for setting the strategy and leading the business analytics team in the Retail, Hospitality, Travel and Transportation industries at Teradata.


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