Introduction to Retailing Analytics
This chapter provides a basic overview of retail terminology and concepts, as a means of defining terms that will later be discussed. It should be a primer for those who have little experience, and provide more experienced readers greater clarity about the author's way of discussing concepts.
Retailer goodwill is the "warm and fuzzy feelings" that customers have toward the brands that serve their needs. "Fuzzy" is particularly important, because the way that people form and experience goodwill is nebulous, and an attempt to measure goodwill with black-and-white precision is not true to the nature of the phenomenon itself.
That is, we can define some of the factors that cause people to feel goodwill toward a retailer, but cannot derive a precise mathematical equation that can be satisfied to create goodwill in all customers.
The result of goodwill is that customers are more likely to purchase from you, more likely to purchase more often, more likely to remain loyal over a long period of time, less likely to shop when they need to reorder, etc.
There are also "soft benefits," so called because they cannot easily be quantified in hard numbers or correlate exactly to a financial gain. For example, their willingness to advocate a product to others leads to more sales, but cannot be directly measured because it cannot be observed, and because the value of advocacy is hidden among many factors that lead a new customer to select a retailer.
The fact that they are not quantifiable and cannot be expressed in sales dollars does not mean that they are not also very important and worthwhile to pursue.
The Inside Scoop: Retail Power Brokers
The most powerful player in the market is the buyer, whose decision to purchase (or refrain from purchasing) creates the revenue for the entire distribution chain. Manufacturers and retailers seek to provide goods and services that appeal to buyers, or convince the buyer that their products are appealing.
In the credit card business: credit providers would do well to remember that consumers buy merchandise, not credit. Credit is an accommodation to make the purchase of merchandise feasible, and no-one consumes credit for the sheer joy of being in debt.
The next most powerful player in the market is the retailer, whose decision to provision a given item of merchandise is based on his prediction of consumers demand for it. In the traditional model, retailers buy inventory and accept the risk of not being able to sell it, shielding the rest of the distribution chain from loss. Increasingly, however, retailers use the rack-job model where the inventory remains the possession of a manufacturer or wholesaler, who bears the risk of unsold inventory.
Also from the credit business: retailers seek to control their costs, so the fees they pay to accept credit cards are a very important aspect of the retailer's budgeting process. They balance the increased revenue they will gain by accepting a card against the cost of accepting it.
"Almost without fail," retailers are set up in a hierarchal arrangement: each store is separated into departments, and each department manages a given line of merchandise. In such arrangements, there is a back-of-house department that deals with credit cards, generally store-branded cards - this firm has little prominence, but significant power.
The author indicates he had better success if he avoided making direct contact with the credit department, but instead made his initial approach to a merchandise manager whose ability to sell goods depends on their ability to extend credit (e.g., the furniture or appliance area of a department store), and get their assistance in reaching out to the credit department. It's a difficult process but "well worth the effort" to be indirect.
Understanding the organizational structure of retail is essential to making approaching the appropriate individual with a prospect to sell a service through that retailer.
Per the earlier point: there is generally top-level management who govern the entire store, management for each line of merchandise (produce, women's clothing, appliances), and management of supporting services common to all departments (credit, shipping, etc.).
In larger organizations, there are likely sub-managers: the general manager delegates appliances to one vice president, who puts separate managers in charge of major appliances, home entertainment systems, and small electronics. Each of those managers may have a number of buyers, such that one person may be the buyer for television sets and nothing else. In very large operations, buying is handled by a distribution center that serves multiple retail locations, or even at a headquarters that arranges buying for all distribution centers in a large geographic region.
(EN: the tail often wags the dog in larger organizations. That is, where buying is handled at headquarters, the individual store manager has no authority over the buyers and is relegated to selling whatever merchandise they buy rather than having input into the buying decision and the ability to represent the desires of the customers at his particular store. This can result in poor service to the customer.)
Since the buyers are removed from the retail operation in larger organizations, most depend on "executive information systems" to provide insight as to what merchandise is selling and which stores. These systems leverage databases ("cubes") that generate reports and dashboards to provide this information upstream. It's a particular problem that these systems do not provide much flexibility - decision makers get standardized reports that may not provide the details they need - and if they want specific information (such as the sale of one line in departments in a specific area), they have to place a special request to the IT department to run a query. This is complicated, difficult, and slow ... but it's "very common."
The level of detail desired generally becomes greater at lower levels of management. Top management wants overall details about sales of all items in all stores; a department manager wants to know sales in his department; the aforementioned television buyer wants to know how each model of each brand is faring in each store. There are far more questions and they are far more detailed.
(EN: And this becomes a problem because authorization for information systems is made at the executive level, such that the systems cater to their broad and general needs but neglect those of lower levels, whose need for information is greater and more critical.)
The author also observes that the term "marketing" is used very indiscreetly in the retail sector. Laying aside the instances in which the term is used to describe a function that is not marketing at all, there are still many instances in which a marketing function deals with subset of marketing. With this in mind, the author provides some examples:
Real Estate Marketing
The real estate department, which chooses and procures store locations, is driven by marketing data - determining whether a given location will be convenient and appealing to customers.
The analysis often involves a team who considers factors such as the population demographics, existing competitors, logistics, and other factors pertaining to physical location, not to mention those who consider the properties of the store building and its situation on the site.
Creative Advertising Marketing
Advertising is a traditional area of marketing and is more readily recognized as such.
Its analysis has to do with analyzing and segmenting the market, then determining the media that will be effective in reaching the intended audience, then designing an appealing marketing message.
This also includes analyzing the performance of marketing campaigns, and sorting out covariance when multiple campaigns are running at the same time, to determine its effectiveness and inform future decisions.
Operations marketing generally considers the behavior of consumers in the store environment, and determining the elements of the store experience that may be persuasive or dissuasive to their purchasing behavior.
Much of this involves attitudinal surveys and observational studies aimed toward improving purchasing behavior. Analysis may involve asking questions through focus groups or surveys to determine things such as the store layout, the merchandise selection and display, the interaction with staff, and other factors influence buying decisions.
The author mentions a few different techniques (mail surveys, store intercepts, etc.) and the pitfalls (the gap between what people say and what they actually do), but it's very superficial.
Direct marketing involves sales promotion, and is differentiated to operations marketing in that its intent is to elicit an immediate sale without making any changes to the store or the products.
The analytics involve defining an audience most likely to respond to an offer, and creating an offer that is more appealing, in order to be more effective in sales promotion and avoid waste (the money you spend to main a coupon for dog food is wasted on households that do not own dogs).
Strategic marketing is often a compilation of other marketing areas, which takes a broad range of time (five years at minimum) and sets the high-level goals for the marketing efforts in the organization.
Strategy involves the definition of the target market and the type of marketing that is effective in reaching them - whether the companies marketing budget is better spent on improving the store experience or on promoting sales. To fully understand their choices, the strategic marketer must have data pertaining to all the various options.
The author suggests that strategic marketing "is not for the weak-hearted individual," as decisions on this level can have a dramatic impact on the revenue of the firm, and senior leadership is intensely interested and highly demanding.
Communicating To The Retail Organization
Knowing the correct terminology is critical to speak to retailers in terms they understand, which demonstrates your own competence in retail.
He throws out a list of terms: case pack, merchandise hierarchy, drop shipping, UPC, gross margin, general merchandise, logistics, markup, POS, price types SKU, and the like.
(EN: There doesn't seem to be any rhyme or reason to this, and my sense is he intends to give the reader the impression that if these terms are not familiar, the reader should study up on them.)
Internal and External Data
To begin analysis of retail operations, it's necessary to understand the bready of data that the retailer collects. For example, on product sales, the retailer may collect data about sales in various ways:
- Point-of-Sale data is stored at the product level: how many items of a certain SKU were sold during a given time frame. There is no indication of who purchased an item or what else was purchased at that time.
- Market basket data is focused on the customer: what SKUs were purchased during a given time frame. Basket data shows the relationships between products that are frequently purchased together.
- Advanced basket data also includes a customer number so that purchases can be tracked over time.
When dealing with analyzing historic data, your analysis will be very limited if the retailer collects only point-of-sale data. When designing systems to feed future analysis, you can recommend collecting more data.
The author also notes that, working as an outside consultant, obtaining access to such data can be a challenge due to trust. His own approach has been to offer an analysis of non-sensitive data and, but demonstrating he value of analysis, getting access to more data.
It is also useful to be able to contrast a retailer's data with external sources (such data is available from Spectra Marketing, ACNielsen, Claritas, NPD group, Trade Dimensions, and other firms). Without industry benchmarks, a retailer may be unhappy with numbers that, in comparison to competitors, are actually quite reasonable.
Better still, make a comparison to the retailers top competitors - though in doing so, you have to consider what analytical tools and methods their competitors are using to have an apples-to-apples comparison.
Data Reflects Action
The value of data is not in the numbers themselves, but in the way that they reflect actions that have been taken by various parties. Consider the amount of data generated when a customer purchases an item, and a ripple is created:
- At the register, the sales transaction records the date, item purchased, other items purchased, and payment method
- The register communicates to the inventory system that the item is no longer in stock, and inventory is debited
- The inventory system decides whether, as a result of the sale, more inventory should be ordered from the distribution center
- The distribution center's systems decide whether an item can be shipped from the warehouse or more inventory must be ordered from the manufacturer
- The manufacturer's system makes a similar decision, to ship from inventory or make more inventory of the item
- When the item is to be made, the manufacturer's system communicates to its own operations as well as the other firms that supply materials for its manufacturing
- And so on.
This consideration includes only the data pertaining to the item itself. There is an entirely separate chain of events surrounding the processing of the credit card transaction, from the moment the card is scanned at the register to the appearance of the transaction in the customer's account.
The author pauses to remark that most customers see purchasing an item as a simple action - but what happens behind the scenes is marvelously complicated.
It becomes even more so when you consider that each step along the way is managed and mitigated: the decision made by an inventory system to replenish inventory is finely tuned to ensure that the retailer (and wholesaler) are within tolerances: not so little that merchandise is unavailable, not so much that an excess of capital is tied up in inventories.
The author also speaks to the evolution of retail with computer POS systems. Not very long ago, inventory in a store was a manual process of physically inspecting the items on the shelves and warehouses, writing numbers on paper, and compiling reports for various levels of the organization. All of the human effort resulted in high-level awareness of the amount in stock, with no correlation to the behavior of individual customers, and a gut-feel assessment of how much inventory might be sold from day to day.
That is to say it was very costly and very inefficient, and all the companies in the supply chain were poorly supplied with information that today is considered indispensible to running an efficient operation.
The Business of Retail Data
In addition to the collection and maintenance of internal data, there are many companies that purchase and sell retail data to grant firms a broader vision of the retail industry. The author mentions the major data firms, ACNielsen, NPD, and IRI, as just a few who collect and aggregate retail data, and it's a very lucrative business.
The value of knowing trends in the industry is as important to planning and decision-making as having complete internal data. The manager of a single store has very limited vision and his conclusions are based on highly idiosyncratic and unpredictable factors; if his decisions are based on intelligence gathered from others stores in the same chain has a broader perspective; if his decisions are based on intelligence gathered from all stores of the same format, not just those in his own company, his perspective is broader still.
In a competitive marketplace, broad intelligence is more valuable still: a store manager can only guess how many units of a given product he might be capable of selling. If he has complete data about consumers consumption in general, the maximum number becomes concretized, and he can then focus on getting a larger share, given his competitors.
On the supply side, selling data to the aggregators can be a profitable operation for retailers. The larger stores - with high sales volume and a breadth of merchandise categories - can generate "quite a bit" of revenue selling their POS data to aggregators. The author indicates that he was able to create a self-sustaining analytics department, the cost of which was completely covered by the revenue generated by selling data to aggregators.