Category Archives: Managers View

The organizational and human issues associated with the formation and deployment of marketing insights, what inputs should be used in Marketing & Pricing decision making to further the organization’s Marketing & Pricing excellence.

Business Intelligence: Five Questions to Ask Your Technology Provider

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Business intelligence can provide enterprises with deep visibility into their data and to extend the range of vision otherwise available to them. Business intelligence tools for better customer relationship management should help enterprises all along the demand chain to have a full and coherent picture of the market for their products and services. The specific requirements will be different from business to business: manufacturers, distributors and retail operators face different challenges and need different solutions from their business intelligence technology. But any enterprise can get a good start in identifying their own particular needs by considering the five following questions when evaluating the merits of a particular BI solution:

#1: Does the solution provide a means for the enterprise to do more with its data than it currently is able to do? How does it deliver this capability, and what makes this approach better than alternatives?

Any business intelligence solution provider is likely to answer the first part of this question in the affirmative: of course we give you the tools to do more with your data. That’s why the second part of the question is such an important follow-up. How does it deliver the capability? What the business user sees when using a BI user interface tool is just the end of a long series of events that starts with the extracting of data from different sources, transforming the data and making it available for the specific discovery, analysis and decision-making activities in which the business users will be engaging. There are a great many decisions and trade-offs that have to be made about the technology that supports these ETL (extract-transform-load) activities. Some choices will make it easier for the vendor to develop and package the solution, but at the expense of the business user’s ease of use. Can your provider show convincing evidence that the technology choices put the interests of the business user ahead of those of the technology developer?

#2: Does the solution provide different stakeholders with a unified picture of demand, at a level of detail to gain a personalized understanding of the preferences, tastes and needs of each customer?

If you’re using business intelligence for better product and customer relationship management then it’s important that they key stakeholders with an interest in the technology will have a single view of the market environment. That’s easier said than done. The problem is not always the massive volume of information, but rather the multiple streams of data that come in from different sources and providers, in different formats and structures. Threading the many strands into a single coherent picture is a significant technology challenge, and you need to be sure that the technology you adopt is up to the challenge. Beyond that, you want to ensure that you will have information at the right level of detail. Beware the “flaw of averages”, where you see information aggregated to a level that loses critical knowledge about the individual customer’s needs and preferences. That loss of knowledge has a measurable cost in money left on the table with each customer-product interaction.

#3: Does it supply the requisite level of domain expertise to provide intuitive business definition addressed to the language, processes and measurement techniques understood by your business users?

Let’s face it: business professionals at any given company, in any given industry sector, speak a particular language. It’s the language of their products, their customers, their processes and their performance measurement techniques. A good business intelligence solution needs to be rooted in a sufficient level of domain expertise to translate the language of the technology into the language of the business. That doesn’t necessarily mean that your BI solution provider has to be a boutique single-industry expert. But it does mean that the core technology capabilities are sufficiently customizable to easily adapt to the functional language of your business environment. Be cautious about off-the-shelf solutions that claim to work as turnkey offerings in any business environment.

#4: Does it allow for rapid time to value without the need for additional investment into technology and resources on your part?

How much pain before the gain? You want to see results early and often, and you don’t want to worry about the need to acquire additional infrastructure or build out an internal IT team to support the technology. In today’s Big Data environment you are probably going to want a hosted solution where the data is warehoused and managed in the cloud. The most important thing to know about a cloud-based solution is that it passes the responsibility of day-to-day management of the data from you to your technology provider, meaning less of an internal administrative/IT burden for you. Then you can focus on the other part of this question: understanding the time frame in which you will be able to see and measure the value delivered by the solution.

#5: Does it adapt and make continuous improvements and facilitate those improvements back into the system for still more informed insights?

The only thing certain about your market environment is that it will change, and often in ways unanticipated when you invest in a new technology. Technology that cannot adapt to new business requirements can go stale very quickly. Does your provider offer a market aware technology? Market awareness is when a technology platform has the capability to learn from new market insights as they evolve, and feed those insights back into the core models and algorithms that power the technology. Market awareness gives you more confidence that your business intelligence solution will be able to rise to the challenges of tomorrow as effectively as it can to the challenges of today.

Beyond Mass Marketing: Knowledge Lost and Regained

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Throughout most of human history the activity of selling had been very personalized and localized. Every seller knew every buyer, whether in the ancient agora or the medieval town square or the frontier towns of 19th century America. Buyers trusted sellers to offer a fair price and sellers trusted buyers to be loyal customers as long as that fair price was maintained. Because sellers knew each customer personally they knew what each customer cared about. With that knowledge they were able to match each customer with the right product, and make offers with a high likelihood of getting the terms of offer right.

Knowledge Lost: The Evolution of Mass Marketing

As economic growth gained pace during the Industrial Age this personalized relationship started to change. New modes of production and distribution opened new markets and new customers. Sellers now had to rely on impersonal channels for fulfilling customer demand. In so doing they lost important knowledge about what customers wanted and the value they put on all the different items in their market baskets. They had to rely on second-hand information and gain insights from whatever signals they could detect along the chain of operations from their factories to their sales and distribution agents to their end customers. But these signals and anecdotal bits of  information were often inaccurate, or misleading, or contradictory. Marketing in many ways became a guessing game. Guessing led to bad decisions, unsatisfied customers and money left on the table.

Sales & Marketing in the Early Information Age: The Flaw of Averages

As the Information Age dawned, businesses welcomed the promise of technology in making their operations more effective and productive. But that promise would turn out to be very unevenly distributed across different business activities. Some, like financial reporting or production logistics, readily lent themselves to automating key processes for more operational efficiency. Sales and marketing, though, was a more elusive target whose key performance metrics were harder to quantify.

One of the biggest challenges faced by marketing science during its embryonic years in the 1970s and 1980s was that sellers were now trying to sell the same products to lots of very different types of customers. With the arrival of enterprise technology it was now possible to record those sales electronically and upload them to a data warehouse. Marketing-focused decision support systems were also making their debut at this time. Marketing managers could analyze this intelligence and use it to make decisions around pricing and other marketing levers. But there was a flaw in this approach – call it the “flaw of averages”. The flaw was in aggregating all the data to a top-level average, and substituting that average for the individual tastes and preferences that remained hidden in the details of the data.

Segmentation and the Average Customer

One of the most widely popular responses to these challenges was customer segmentation, an approach that gained popularity over the 1980s and 1990s. Segmentation attempted to group customers into buckets based on a handful of common attributes. But segmentation was not a remedy to the flaw of averages. It still ran on the basis assumption of the “average customer” – and even at the segment level there is no such thing as an average customer. Segmentation did not bring back the knowledge that was lost when marketing ceased to be a highly personalized one-to-one experience.

But not everyone was swimming along with the currents of customer segmentation. In the late 1990s there were small enclaves of engineers and business solutions architects (including some who would go on to be founders of Sentrana) who were exploring a different path: using mathematical approaches like Hierarchical Bayesian modeling to solve problems at the level of the micromarket. “Micromarket” was a concept that had as its basis the town square of old: a venue where buyers and sellers all know each other. The idea was to extract information at this level – for example by the scanner codes employed at individual retail establishments – and apply powerful mathematical modeling to generate insights at that same level of micromarket detail.

Knowledge Regained: Data-driven, Personalized Marketing

This was an approach that could actually solve the flaw of averages. In effect a data-driven micromarket approach can help enterprises regain the knowledge that was lost when marketing left the town square. This is accomplished with information from multiple sources, in multiple formats, at multiple levels of activity to provide enterprises with information about their customers and information about their customers’ customers. That’s information they can not only analyze, but from which they can make decisions, and execute on those decisions, with the ability to make more personalized connections with more customers than ever before.

Selling in the Age of Big Data

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Every day we see more evidence that the age of Big Data has arrived. With the data inevitably comes automation – the steadily increasing automation of business activities that used to be performed by human beings. For companies with a strong sales culture this presents a deep-seated challenge. Business analytics and data-driven sales & marketing tools can offer deeper insight into your demand environment and provide recommendations to make better decisions around your products and customers. But as an organization you worry – do these data-driven capabilities really make your organization better if they threaten the very foundation of your longstanding sales culture? You don’t want a technology solution to replace your sales force. What you want is a technology solution that can make your sales professionals better at the job they already do.   Continue reading →

Big Data: It’s Not Just About Size

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Are you ready for the era of Big Data?

The phrase “Big Data” is now firmly part of the mainstream business lexicon. You hear about it with increasing frequency, but what does it really mean for you and your business? The answer is: a great deal. Big Data not just another lazy buzzword. It has the potential to revolutionize your industry value chain and upend practices that have been in place for years. The revolution has not yet taken root as firmly in many B2B markets as it has among retail consumer-facing enterprises, but that is fast changing. How do you get ahead of the curve and be in the position to profit from the changing role of technology in business decision making? A good place to start is by understanding what Big Data actually is, and how it relates to your company, your customers (and their customers), your suppliers (and their suppliers) and all your other key business partners. Next is to understand what capabilities you currently have and what gaps exist that need to be filled for a full-fledged Big Data capability consistent with the specific characteristics of your demand environment. Continue reading →

Managing Information in the Age of Big Data

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Big Data presents challenges for business managers and IT pros alike

The Big Data revolution taking place across many industry sectors is presenting challenges to business managers in traditional line functions like production and marketing, and to corporate IT professionals alike. Big Data goes to the heart of the IT professional’s domain – the way that the enterprise warehouses and manages information – and potentially upends long-established systems and practices. At the same time it presents business manages with a new level of urgency to incorporating deep data analytics into their daily array of tactical business decisions. As important as it has always been for business and technology professionals to be on the same page, it is now more important than ever. Both sides also need to understand the specific concerns and challenges that are keeping the other up at night, and work together to solve them and identify critical gaps. Continue reading →

The Myth of the Average Customer

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there are no average customers

Imagine that you have two customers to whom you are selling beef. One is a greasy-spoon hamburger joint and the other is an upscale French bistro that offers a pricey version of steak tartare as one of its gourmet entrees. In figuring out the terms of offer for each restaurant, would you create a profile of an “average customer” based on these two data points and sell a single variation of medium-grade beef at a mid-range price to both the burger joint and the classy bistro? Of course not – you know perfectly well that approach would please nobody and result in two lost sales. Yet that is exactly what sellers in many industries are doing every day – marketing to some notional “average” customer that in reality does not exist. It may seem less absurd when the average is based on thousands or tens of thousands of customers as opposed to two – but it is no less flawed as a marketing approach. Every single customer has a unique set of needs, preferences and priorities. It is your job as a seller to tailor your offers as close as possible to each unique demand curve. Fortunately, technology and science make that daunting challenge possible.   Continue reading →

Smart Promotions: It’s About Timing

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You have crunched and analyzed the data. You have homed in on an assortment of specific products to offer to targeted customers with a defined set of attributes. You have built a promotional campaign with introductory prices to entice these customers to purchase items from you that you are confident they are currently buying elsewhere. You even have prepared attractive sales collateral emphasizing the products’ attributes you believe are most closely aligned with the customers’ needs and preferences. But there is still one important piece of the puzzle you have not put in place: When is the right time to make the offer? Predictive science can help sales and marketing decision makers improve the likelihood of launching promotions to coincide with a customer’s willingness to change his or her current buying habits and accept your offer.   Continue reading →

Increasing Demand in a Flat-Growth Environment

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Economic growth in the US continues to face many daunting challenges. Companies across a wide range of industry sectors are experiencing top-line sales growth that is anemic at best, and in many cases negative. Foodservice is no exception: belt-tightening by households certainly impacts the food away from home sector. In the absence of the natural demand increase provided by a growing economy, what can enterprises do to improve their top-line performance?

certain products go together

At Sentrana we believe that companies can increase sales, even in tough economies, by understanding their own demand environments at the most detailed level possible – in other words, to be able to predict what products to offer to what customers, and to use insights from available sales data to make targeted recommendations around pricing, promotional activities and timing. In foodservice hundreds of thousands of products pass through any given distribution channel every day to hundreds of thousands of restaurants and other operators. To meet this challenge effectively manufacturers and distributors need to contribute their respective insights about products and customers onto a common platform from which to obtain a full picture of demand. Recently this has motivated prominent industry players to collaborate in managing performance across key product categories. Continue reading →

Working Back from the Point of Sale

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Solving Three Key Challenges to Profitable Category Management

Managing product categories for optimal performance in foodservice presents three key challenges that category partners need to solve: how to manage data reporting and analysis, conduct effective selling logistics, and close the sale. This post examines these three problems and identifies practicable solutions for manufacturers in collaboration with their distribution partners.

Data Reporting, Management and Analysis

Manufacturers often do not have regular, dependable access to sales data. Transaction information typically resides downstream, so the manufacturer must negotiate with its distribution partners to establish a mechanism for information sharing. Assuming such agreement is reached, the process may give rise to a variety of data problems. Data integrity issues are prominent among these. It is unlikely that the manufacturer will receive specially prepared sales reports – information more probably will come in the form of raw data untreated for accuracy, correctness or clarity. Readers of these reports will find it hard to obtain insights in them from which to take action on a timely basis. Continue reading →

Category Management: An Antidote to Trade Spend

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Trade spend outlays continue to dominate the sales & marketing budgets of foodservice manufacturers. This is despite a persistently high level of dissatisfaction with the cumbersome administrative burdens of trade spend programs and the lack of measurable results. Manufacturers want a clearer understanding of how targeted trade promotions influence downstream demand, but instead they become enmeshed in unproductive administrative paperwork such as resolving and processing duplicate claims.

The current trade spend paradigm also does not work in the best interests of distributors. While they do benefit in the short term from the financial impact of the trade dollars they receive from their suppliers, distributors do not obtain insights from current trade spend practices that could help them more effectively grow demand across products and categories. Of more benefit would be product and assortment education from their suppliers, enabling them to identify tangible ways to tap into new sources of sales growth.

Category management, a standard practice in many retail sectors that is now gaining currency in foodservice, can be a way to attain this knowledge, use it to effectively drive growth for both manufacturers and distributors, and ultimately to phase out the unproductive aspects of the current trade spend paradigm. Continue reading →