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CPG Manufacturers Leverage Data to Collaborate with Retail Partners: A Spotlight Q&A with Justin Honaman of Teradata

Originally published October 24, 2013

This BeyeNETWORK spotlight features Ron Powell's interview with Justin Honaman, Industry Consulting Partner, Consumer Goods, at Teradata. They discuss how data-based collaboration and analytics are transforming how CPG manufacturers and retailers drive value to the consumer.
Justin, let’s start with general consumer packaged goods/retail collaboration trends. What’s trending?

Justin Honaman: First, just in terms of how we’re set up at Teradata, we focus on the industry from the standpoint of not only the consumer goods space, but also the collaboration with the retailer space. If you think of customer strategy from that perspective, there are several trends that resonate. The first trend is the “defining of big data” and what it means to the consumer goods organization. It’s playing out in different ways depending on the organization with whom we are working.

The second trend is cloud, software as a service, insights and analytics as a service, which obviously has been hot for a couple of years now. It’s just now evolving into more of a mainstream strategy with CPG. And, cloud-analytics solutions are challenging the traditional methods of hardware / IT-centric insights solutions.

A third area is one that is really interesting is one that is still evolving. It’s the data discovery space – the area where you hear lots of chatter about how data scientists are pulling together structured and unstructured data to drive business decisions.

Can you tell us more about the data discovery space?

Justin Honaman: The data discovery place is a new space within CPG and one where finding the right resources is a challenge. Data discovery (or data science) skill sets go well beyond those of your typical business analyst. And, if you think about what it really means, it’s the science of what’s behind the insights. And you have to think beyond just rows and columns and pulling together information on a spreadsheet and delivering that as a report – which is the typical business analyst’s role. Data science, and the data scientist role, is understanding that I have access to specific data elements. I can bring those elements together in an environment or a sandbox where I can try different queries and build scenarios. The results may be expected…or unexpected and those, in turn, may drive a new view of the business or methods of making decisions. So I find it a really interesting area, and it will be one that will drive business process change within not only consumer goods but other industries as well.

From a data integration perspective, we’re seeing the need for integrating data to gain shopper insights, loyalty and so on. Would you like to comment on that and on social media?

Justin Honaman: Teradata’s bread and butter and core competence is and has been centered on managing large volumes of data and doing it extremely well. So many times, when organizations reach out to Teradata and our team, it is because they are looking to solve a data integration or data management challenge or they need help with understanding how to utilize many different data sources in a way that makes sense for the business.

In consumer goods, a primary focus is on how to further evolve collaboration with retail partners. This involves making better use of supplier sales data, point-of-sale data coming from the retailer or from an aggregator, syndicated data (Nielsen, IRI), category management data, ad tracking data, social listening data, and trade promotion data. And, even further, some retailers are willing to provide loyalty card data, and the consumer goods organization must determine how to make use of that as it relates to other available sources.

We’re seeing an explosion of interest around this data aggregation and integration space – all of it, by the way, in the cloud and all of it through software as a service or a monthly subscription type arrangements. Some in the industry would call the solution in this space a Demand Signal Repository (DSR) but today’s business needs are different than the original definition of DSR in that the solution to a CPG problem may not require full integration of data for every retailer but instead only require a subset of data to enable collaboration decisions. Like retailers, CPG organizations are moving quickly to invest in fast-start / fast-fail cloud-based marketing and sales solutions to complement their existing “heavy IT framework” capabilities, thus enabling new and different insights and thereby driving business decisions.

Justin, what’s the general state of database collaboration between CPG manufacturers and their retail partners?

Justin Honaman: It varies and depends largely on the retailer. For example, some retailers are more than happy to provide line-item detail and daily point-of-sale information to the CG supplier so they can create a better order, more effectively manage replenishment, manage out of stocks, ensure on-time/in-full delivery, and adjust forecast to meet the demand of what is selling in the store. From a retailer perspective, when the CG supplier invests in back-office capabilities that make use of point-of-sale data, that’s a win. It is a huge opportunity. The retailers are further along in terms of their investment in this space than CG suppliers (and have been for several years). CG suppliers now are looking to catch up from an infrastructure (and business process) perspective to manage and utilize point-of-sale data to drive collaborative retailer interactions.

The other area where I’d say there is great collaboration potential between retailers and suppliers is in the use loyalty data (including loyalty card data) to have a better view of shopper preferences.. This is especially useful for those select CG suppliers that are infusing this data set in the product innovation process.

Finally, many CG suppliers are adjusting their joint business planning process (JBP) with retailers. For example, most large CPG companies have joint business planning meetings and working sessions with Target, Walmart, Safeway, Kroger and down the line annually and promotions are typically planned based on historical sales activity. They put a plan in place and are not flexible in their ability to dynamically modify promotions throughout the year based on shopper preference or demand change. But with better access to data and information and faster access to what’s happening at the point of sale, the JBP process can be changed and be more flexible and dynamic. With forward-thinking CG companies, we are seeing the JBP process change to become a more dynamic, flexible, and interactive routine.

Database collaboration sounds complex. What have you seen as the best approach to get started?

Justin Honaman: There are plenty of BI best practices out there in terms of how to acquire and make use of multiple data types, but the biggest challenge preventing forward progress within consumer goods organizations is that business processes that utilize insights are not flexible or able to leverage new analytics and data inputs. Change requires investment and depending on the organization leadership, change may not move at a pace needed to keep up with advancing retailers.

An example is in the area of forecasting. If I’m operating at 60% forecast accuracy in my current process, and I’m not willing to invest in new capabilities that will leverage newly available retailer data, my process will not improve – I just end up paying for more storage of unleveraged data. A big challenge in most consumer goods organizations is the investment in changing business process to make use of better information available to the organization.

DSR – demand signal repository – is very popular. Has it lived up to its promise?

Justin Honaman: The DSR concept is a good one – and one that has been the “buzz” of CPG the last 4 to 6 years. The basic concept of a DSR is the collection, storage, harmonization, and utilization of multiple disparate retailer data types in order to improve collaborative business handoffs with retailers. Data types typically include – shipment data, point-of-sale data, loyalty data, syndicated data (e.g., Nielsen, IRI), ad tracking data, population, and weather data.

The problem is that it is a challenge to tie these disparate data types together. Hierarchies differ, data arrives and/or is available at differing times, metrics in the data are not consistent with those in a source sales system, and the level of detail of data that you get from one source may differ from others. So I’d say that the DSR concept has not lived up to its billing.

From a technology provider perspective, many companies (e.g. Teradata, Oracle, IBM, SAP) have pursued the role of “grand aggregator” of retailer data thereby requiring CG suppliers to subscribe to data access (pay a monthly fee for harmonized data). Retailers have not bought in and therefore each link between supplier and retailer is custom-built and differs based on the source systems in place at both.

Justin, you know business intelligence has been around for many years now. We started with traditional BI. We’ve had these major trends, the latest being big data, and now we’re moving into analytics. Many in our audience are very familiar with BI, and they’re hearing that they really need to move forward with analytics. How do they start?

Justin Honaman: If you think about this space, the information exploitation concept has been a top priority or near top priority for organizations since the early 90’s. When I was in consulting in the ‘90s, we called it performance management and this included balanced scorecarding. As the CRM space took off in the late 90’s, insights became an important part of the CRM strategy for primarily B2C organizations. Insights driving action was the mantra of many technology organizations. As data storage and utilization technologies have evolved, we see not only data management technologies evolving, but also the data utilization and analytic creation businesses thriving. As social and mobile have taken off, so have the need for technologies to harness, make sense of and utilize this data with the more standard already-available data (e.g. sales). The start-up market for BI / analytics is hot – maybe the hottest it has been since the dot-com boom.

In terms of getting started, you have to first define what problem you’re trying to solve. Based on that defined problem, you must then define what data enables the associated business process and where that data is located. Next is evaluating options for bringing that information together in a way that makes sense. This is where I like to start with both new and existing customers as it grounds everyone in where we are, what problems we are going to solve, and ensures alignment on approach to solving that problem. We must provide a good answer to “why” it is important so as to ensure long-term solution / capability viability.

For CPG and retail what are some of the unique analytics you feel are important, especially as it relates to sales and marketing?

Justin Honaman: The first thing to understand is that BI and analytics traditionally have been “owned” by IT. That’s where it started. And it was always a data warehouse with a reporting tool on top. Over time, the business (e.g. sales, marketing, supply chain) evolved their own home-grown BI solutions (aka “spreadmarts”) due to limitations in the corporate system and cobbled together views of information to enable the business to make to make decisions. What you’re seeing now is the CMO’s office – shopper insights, consumer insights, category insights, space planning, revenue management, integrated marketing, commercialization, etc. – is becoming more important and taking on a larger role from the standpoint of analytics and integrated business intelligence. That’s the first thing to understand – the business side of the house is playing an increasingly larger role in the strategy and direction of analytics and business decisions using information. We have seen this in retail and are now seeing it in CPG. Marketing and Sales have OPEX dollars to spend…IT has limited CAPEX dollars to invest.

An example is in the area of consumer insight. When I say consumer insight, what I mean here is that CG suppliers are now looking to get closer to the consumer. I’d hesitate to say they’re trying to do one-to-one marketing, although that is happening where they have a loyalty program. CG Suppliers through their loyalty programs (e.g., Coke’s MyCokeRewards) are talking directly to consumers. Consumer insight is an area that is evolving quickly and includes components of CRM, digital marketing, analytics, and product innovation. It also includes basic elements like providing coupons and brand content to the consumer or differentiated experiential opportunities. Most CPG suppliers are not playing in this space (yet), although they’re talking about it.

A second area to consider is the Shopper and Category Insights space. Analytics and algorithms are needed to enable store segmentation, evaluate brand switching levers, and also to analyze shopper trip mission details, assortment requirements, and market basket trends. Some of this is available via syndicated sources but the data set and analytics are limited – and expensive.

At Teradata, we’re obviously not a trade promotion vendor, but what we find is that we’re an important enable to the trade promotion process in place within CPG. We bring together large volumes of information, process and make sense of it quickly. We crunch it, we run it through algorithms and then we provide input to the trade promotion solution that might be in place within the consumer goods organization.

Things that we’re helping to do through our category capability are things like promotion decomposition. I mentioned the brand/package switching capability and we are also looking at effective retail pricing to determine the trigger point for a consumer to make a buying decision. And then assortment optimization is another space we’re playing in.

That gives you a framework for a couple of different advanced analytic segments where we are focused.

You mentioned big data and a lot of companies might look at point-of-sale data as big data, but what are you seeing with regard to big data initiatives within retail and CPG.

Justin Honaman: While the industry has settled on a common definition for big data, most CG organizations are tweaking the industry definition to accommodate their own individual analytic needs. The definition of big data is standard and most IT organizations define big data the same way – volume, variety and velocity. But what I’m finding and what’s very interesting is most of the consumer goods companies we’re working with have assigned a team or individuals to “figure out” big data and what it means to them. We’re seeing that consistently.  And they’re using the words “big data” to frame up projects they’d like to get done that really aren’t big data. For example, just because there is a lot of point-of-sale data coming in from retailers, doesn’t mean that that is a big data initiative. It means there’s a large amount of data, but it doesn’t mean it’s big data. It’s very interesting, and the definition is different depending upon who we’re talking to in the consumer goods space. The majority of times, though, the organizations that are talking big data are looking at it the right way in terms of the possibilities of bringing together various sources in a way that makes sense and leverages the power of a tool that can aggregate, integrate and harmonize. I think that’s the power that can be found in big data. It will continue to be a trend in the same way that cloud is now playing out in a big way with most companies. In terms of leveraging big data across social, mobile, POS, sales, syndicated and loyalty – that will be how consumer goods companies think about it.

Consumer goods companies have done a lot with transactional data. There’s a big trend toward all of this consumer data. Could you elaborate a little on the consumer data side?

Justin Honaman: From a consumer goods perspective, consumer data is really centered on what can be learned and known about the actual end consumer and not what is known about the retailer. For example, where you’re seeing interest in consumer data is where a large organization has many different brand websites, they are engaging consumers in a dialog – even providing, as I mentioned earlier, coupon downloads or opportunities to get recipes online or be part of a loyalty program. Therefore, the consumer has to register or opt in, and at this point, the CG supplier has “known” consumers that are at least one step more engaged than your unknown consumer. And if the CG supplier wants to talk directly to the consumer, they have to have an infrastructure to handle that and also the capability to make sense of information that is coming through interaction outlets. Then you add in the typical social sites – Facebook, Twitter and LinkedIn, wikis and blogs. That’s where you cross over into the big data space, which is pulling together structured and unstructured data.

Retailers have already figured it out. Many of them are already doing one-to-one marketing and managing shopper interactions because they have to. They have to create a differentiated experience that keeps shoppers shopping in their stores and not defaulting to Amazon.

Consumer goods suppliers are a bit behind in this space. As is typical, retailers lead and consumer goods suppliers follow. It’s a matter of defining the right capabilities to solve the right business problems so that the business can have access to information quickly. That’s where the consumer insight space is becoming stronger.

You mentioned wikis, blogs, Twitter, Facebook and so on. What kind of approach would you take to be able to handle all of these new sources of information?

Justin Honaman: There are plenty of technology service providers that can very quickly provide what they call social chatter or social listing, social sentiment or brand sentiment. They have tools that search and pull information and can very quickly provide insights into brand, product, package, customer – the overall company brand and what not. Honestly, that’s where I’d start because they’re already good at it. For a lot of consumer goods organizations, their agency can provide this information through their agency model. It’s not something you build yourself.

The question for the business is once you get the data, what are you going to do with it? Why do you care? If the idea is to understand what’s trending so you can plug it into your new product innovation process, that’s interesting. It might be just social sentiment around specific flavors or preferences in different parts of the market – North America, for example. Or it can truly be a forecast indicator of what’s to come. Most of the data that I mentioned that can come from websites and other touchpoints is siloed and typically managed by an agency in the cloud, available through proprietary reporting tools and mechanisms, not integrated, and therefore difficult to leverage in a consolidated manner. That’s the current state today.

What best practices are you seeing around retailer/supplier collaboration?

Justin Honaman: There are many untapped collaboration opportunities between retailers and suppliers. There is an opportunity for growth with new analytics and new information that’s available not only to the retailer, but also the consumer goods supplier. In the past, CG suppliers leaned heavily into the retailer who owned the direct shopper/consumer relationship. Consumer goods organizations – specifically marketing, sales, and customer teams – have great information that can be used to help the retailer in driving additional sales in store, driving differentiated experiences in stores, and the consumer goods supplier now has better information about their products, about what products should go in which geographies, about which consumers are buying their products or prefer their products in specific stores, and that is powerful.

What they can now do is customize their assortment in a better way, press for brand growth against private label, make the overall partnership with the retailer stronger because they have better information and are bringing better and new ideas to the table. If you think about the historical relationship between retailers and suppliers, it’s always been based on historical sales. The problem with historical sales data is that it doesn’t take into account outlying factors and now certainly doesn’t take into account the social and mobile influence on consumers. The partnership between consumer goods supplier and retailer is evolving and is going to get to a place where information moves very quickly between the organizations and between the teams that manage the partnership between these organizations. I think it’s an exciting time. I’d say the next two years, thanks to better access to data, better integrated data and better analytics on top of data, the overall retailer/supplier partnership is going to evolve in a way that it hasn’t in the last six to ten years.

I couldn’t agree with you more. Thank you, Justin, for providing our readers with very useful insights into how CPG manufacturers are leveraging data to collaborate more effectively with their retail partners.

  • Ron PowellRon Powell
    Ron is an independent analyst, consultant and editorial expert with extensive knowledge and experience in business intelligence, big data, analytics and data warehousing. Currently president of Powell Interactive Media, which specializes in consulting and podcast services, he is also Executive Producer of The World Transformed Fast Forward series. In 2004, Ron founded the BeyeNETWORK, which was acquired by Tech Target in 2010.  Prior to the founding of the BeyeNETWORK, Ron was cofounder, publisher and editorial director of DM Review (now Information Management). He maintains an expert channel and blog on the BeyeNETWORK and may be contacted by email at rpowell@powellinteractivemedia.com. 

    More articles and Ron's blog can be found in his BeyeNETWORK expert channel.

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