Big Data in Retail & CPG Requires a Scalpel, Not an Axe

To satisfy the evolving demands of customers, Chief Commercial Officers need to wield big data in Retail & CPG using a precise scalpel rather than a blunt axe.

Chris Newbery
Chris Newbery
24 janvier 2021 4 min de lecture
Big data and analytics in CPG and Retail requires precision
To satisfy the rapidly evolving demands of customers, Chief Commercial Officers need to wield big data in Retail & CPG using a precise scalpel rather than a blunt axe. Category Management is among the biggest consumers of data but is constrained by disconnected business processes and onerous data wrangling which severely limit current capabilities. To survive and thrive in today’s dynamic retail environment, a new perspective is needed.

Retail & CPG CCOs and their category planning teams already utilise big data to help inform decisions on the crucial mix of products, prices and promotions to drive revenue, profit and customer satisfaction. The problem is that much of this data is historic and aggregated, requiring heavy manual processes to collate, prepare and then analyse for insights. Data is extracted from multiple systems, massaged and analysed (typically in Excel), and then used to support basic analytics for reporting and decision-making, before updating operational systems for execution. These unwieldly and onerous processes drive “one-off” events, such as annual range reviews, or an update of pricing strategy, and constrain the level of detail at which decisions are made.

This simply won’t continue to work in today’s ultra-competitive market. Retailers need to be dynamic, and react to changes at global, national and store level in near real time. A data mindset that illuminates what customers buy and how they buy, rather than how well products are selling, is crucial to success in today’s environment.

Wield a scalpel rather than an axe

Localisation has been a buzzword for some years, as Retailers & CPGs have found the scale benefits of identikit stores and national ranging decisions quickly undermined by the refusal of customers (and local competitors) to act in a homogenized manner. Then came clusters of stores appealing to clusters of customer segments which helped but are still not responsive enough to cater to today’s shopper who expects a more personalised service and more relevant offers, every time and in every channel.

To meet these challenges category management needs to start with people, not products. After all, the ultimate goal is to change customer behaviour in ways that increase sales, margin and lifetime loyalty. The more granular your understanding of that behaviour, the more likely you are to be able to affect it. By understanding what customers really love, you can start to orchestrate ranges, pricing and promotions to appeal precisely to every customer in every store across every channel.

By combining store, product and customer data to understand the actual behaviour of real customers, rather than relying on segments of data, the promise of localisation can be realised. Instead of annual top to bottom range reviews that are time consuming and blunt, ongoing adjustments can be made at a store-by-store level based on up to date data. Store assortments will then more accurately reflect the preferences, market trends, prices and expectations of those customers who actually shop with that Retailer. For example, in today’s digitally enabled world, promotions can be hyper-personalised, instant and specific location based, targeting individuals and building loyalty whilst shifting the right products at the right price to drive profitable growth.

Automate for speed & FLEXIBILITY

Data consolidation and integration is the essential pre-requisite for this level of sophistication and customer understanding, and automation will be the key driver to realising significant benefits. Building robust, reusable analytics models, running on an integrated data platform supporting operational processes, transforms what can be done. Models can be created to look for anomalies or deviations from expected behaviours or plans. Rather than searching for needles in haystacks, Category Managers can set up predictive analytics to inform them when category performance deviates from what’s expected, understand why, and then instantly do something about it.

Creating an integrated data platform, rather than extracting subsets of data for analysis, means the insights and actions can be automated. Opportunities to change prices can be identified, modelled and analysed in minutes rather than months. The impact of a promotion can be predicted and modelled in advance, considering the potential impact on customer behaviour, cannibalisation, revenue, profitability and margin across each store and the business as a whole. Predicting problems in advance allows early interaction and subtle course corrections, a targeted promotion or intra-day price change. Abrupt last-minute discounts, unexpected out of stocks, or other measures that often have unintended or unforeseen consequences can be avoided.

This is a major value-creating shift and potential game changer. Armed with this consolidated view of what customers are really doing in each individual store in near real-time, the CCO can dramatically speed up decision making across each and every category, in each and every store. Automation of the day-to-day management of prices and promotions, and replacement of top-to-bottom range reviews with ongoing monitoring and predictive analytics, frees up time for more value adding strategic tasks. Sophisticated analysis such as customer pathways, basket drivers and association impacts that were previously complex one-off projects, now become regular and routine.

The focus on customer behaviour, underpinned by integrated data and the automation of day-to-day tasks, will transform the role of both Category Managers and CCOs. Freed from a routine of time-consuming annual range reviews and calendar-driven planning, they can focus on scenario planning, future strategy and exception handling. With the turmoil in Retail & CPG unlikely to end soon, agility, experimentation and responsiveness to customer needs will be paramount. An integrated data platform is essential in providing the foundations required to accomplish this, allowing you to finally replace the axe with a sharp scalpel.

À propos de Chris Newbery

Chris Newbery leads the Retail & CPG Industry Consulting practice for EMEA. Working with major global Retailers & CPG's to deliver high value business outcomes, strategy and thought leadership to achieve Architecture, Advanced Data & Analytics, Supply Chain, Manufacturing, Finance, Marketing & Commercial excellence, through Teradata's software, services, consulting and partnerships. Before joining Teradata, Chris has driven growth for leading Retailers and global CPG's since 1998, with a cross-functional background having worked in Consulting, Customer, Marketing, Digital, Operations, Commercial & Merchandising roles, across all online and offline distribution channels, in multiple countries. Voir tous les articles par Chris Newbery

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