Comment: Category management in an omnichannel world
Shoppers are undertaking ever more complex journeys when buying goods, which straddle numerous channels, while also becoming increasingly demanding in terms of how they expect to be serviced by retailers. This includes both a need for immediacy of delivery and the tailoring of offers to their specific preferences.
The result of this revolution in customer behaviour is that the old ways of category management are reaching their sell-by-date. They are being superseded by a new way of working. From traditional task-driven methods, based on past sales, the industry is gradually shifting towards a more customer-centric methodology that adapts to real-time demand and customer behaviours by utilising a myriad of data sources.
This represents a marked strategic change for the retail industry that has to date largely recognised category management as simply a spreadsheet juggling exercise that uses scant amounts of data beyond the basic information that flows from a Point-of-Sale system. As data becomes more widely accessible, and in turn an increasingly vital business tool for retailers.
The profusion of data sources and the growth omnichannel retail are combining to drive significant change. Consumers are saying to retailers: ‘meet my needs regardless of whether I am in-store, at home, or on the move using my mobile devices’.
Mobile is certainly playing an increasingly impactful role. It is likely that this year’s Black Friday sales will exceed those of Cyber Monday because many people will be in-store on their mobiles comparing prices.
This ‘always-on consumer’ is fuelling an immediacy that is forcing retailers to adapt to remain competitive. To meet the new customer needs requires radical action (all based on available data) to be taken in the supply chain.
Making sense of data
This boils down to three key points: an ability to anticipate demand; the alignment of assortments with local demographics and shopping patterns; and the translation of assortment decisions into executable space plans that optimise space allocation.
For these to be deliverable on the shop floor requires category management to involve the whole supply chain. This means there needs to be greater collaboration between suppliers and retailers. They both need to work together on a new breed of KPIs (Key Performance Indicators) as well as working together to better understand the segmentation of customer bases.
Fundamental to this is customer data – regardless of whether it is sourced from an in-house CRM system or loyalty programme, or bought in from a third-party like Dunnhumby – that all contributes to the ‘secret sauce’ of the retailer or brand owner. This appetite for accessing customer data and the visibility it gives on demand patterns is undoubtedly behind the recent deal by Unilever to buy Dollar Shave Club for $1bn.
But it is not just about access to data that is an imperative today. It is a blunt instrument unless rich insight can be derived from the raw data. Hence data science needs to be thrown into the mix. New capabilities like machine learning are developing at a rapid pace and will have a growing impact on category management as it will provide the indicators of what actions to take.
It will help create a better understanding of the market and enable a greater sensing of demand through more predictive capabilities. The fact is retailers need to leverage the available consumer insights to support increased localisation and personalisation as well as dynamic pricing and improved merchandising.
The major grocers have recognised the need to take onboard this thinking as they come under increased pressure from discount chains. They need to more efficiently manage their inventories across different store formats – through localising their assortments.
Such moves towards localising the offer is putting a strain on retailers and manufacturers who are finding the old way of using planograms is simply not sustainable. The need for constant generation of new planograms to support their increasing desire for localisation strategies is highlighting the fact that automation needs to be introduced into their systems. This will reduce labour-intensive human touch-points and lead to real-time capabilities within the category management function.
This is not some dream-like scenario that will only come to fruition many years into the future because, for instance, we are already seeing the early signs of assortment planning being re-invented.
Next-generation retail planning
The next generation of retail planning solutions will enable retailers to think like their customers shop –with data science scoring of individual items in the assortment by customer segment and cluster, using historical buying behaviours to forecast their predicted performance. This will enable planners to align product selection with customer preferences while maximising sales, margin and inventory productivity.
To help the industry progress along this journey towards creating the next generation of category management solutions, we are starting to see increased cross retailer collaboration, as they strive to create a framework of best practice.
Although such collaboration is an acknowledgment of the magnitude of the challenge ahead it also highlights that the category management industry is working hard to create future solutions that will deliver on the increasingly complex demands of both customers and retailers.
5 Minutes With: Jim Bureau, CEO Jaggaer
What is data analytics, and why is it important for organisations to utilise?
Data analytics is the process of collecting, cleansing, transforming and analysing an organisation’s information to identify trends and extract meaningful insights to solve problems.
The main benefit for procurement teams that adopt analytics is that they’re equipped to make faster, more proactive and effective decisions. Spend analysis and other advanced statistical analyses eliminate the guesswork and reactivity common with spreadsheets and other manual approaches and drive greater efficiency and value.
As procurement continues to play a central role in organisational success, adopting analytics is critical for improving operations, meeting and achieving key performance indicators, reducing staff burnout, gaining valuable market intelligence and protecting the bottom line.
How can organisations use procurement analytics to benefit their operations?
Teams can leverage data analytics to tangibly improve performance across all procurement activities - identifying new savings opportunities, getting a consolidated view of spend, understanding the right time for contract re-negotiations, and which suppliers to tap when prioritising and segmenting suppliers, assessing and addressing supply chain risk and more.
Procurement can ultimately create a more comprehensive sourcing process that invites more suppliers to the table and gets even more granular about cost drivers and other criteria.
"The main benefit for procurement teams that adopt analytics is that they’re equipped to make faster, more proactive and effective decisions"
Procurement analytics can provide critical insight for spend management, category management, supplier contracts and negotiations, strategic sourcing, spend forecasting and more. Unilever, for example, used actionable insight from spend analysis to optimise spending, sourcing, and contract negotiations for an especially unpredictable industry such as transport and logistics.
Whether a team needs to figure out ways to retain cash, further diversify its supply base, or deliver value on sustainability, innovation or diversity initiatives, analytics can help procurement deliver on organisational needs.
How is data analytics used in supply chain and procurement?
Data analytics encompasses descriptive, diagnostic, predictive and prescriptive data.
Descriptive shows what’s happened in the past, while diagnostic analytics surface answers to ‘why’ those previous events happened.
This clear view into procurement operations and trends lays the groundwork for predictive analytics, which forecasts future events, and prescriptive analytics, which recommends the best actions for teams to take based on those predictions.
Teams can leverage all four types of analytics to gain visibility across the supply chain and identify optimisation and value generating opportunities.
Take on-time delivery (OTD) as an example. Predictive analytics are identifying the probability of whether an order will be delivered on time even before its placed, based on previous events. Combined with recommendation engines that suggest improvement actions, the analytics enable teams to proactively mitigate risk of late deliveries, such as through spreading an order over a second or third source of supply.
Advanced analytics is a research and development focus for JAGGAER, and we expect procurement’s ability to leverage AI to become even stronger and more impactful.