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How Suggestive Selling Can Increase Sales
In the hard goods industry, purchases are need based. Customers buy items for their job and projects.
When a customer comes to the checkout counter, the counter man can look at what is being purchased and recommend other items that the customer might want to purchase. Not all of these recommendations will be accepted, but those that are represent additional revenue. The recommendations that are made depend on the sales person’s knowledge of the products and experience on how they are used. The profit margin on suggested items, when they are bought is very high, because you are leveraging a customer who is already in your store!
Suggestive Selling is a Recession Buster!!
In a virtual environment such as a web-store, there is software that monitors what is being placed in the “shopping cart” and make recommendations based on the individual items or on collections of items. As in the real store, a few of these recommendations will be accepted. But the net effect will be to increase the average number of items in a shopping cart, and therefore revenue.
You might be wondering how software can make these recommendations. It is accomplished by analyzing earlier sales and discovering what items are typically bought together. Emcien’s analytics on the sales data will reveal items that are bought together for the job. For example – 90% of the times item A is bought with item B and C. If you know the items that go together (for a job), why not make suggestions to the customer? This shows that you know how customers use your products; it improves customer service and increases sales. A win-win all the way through!
Distributors typically sell tens of thousands of unique items across hundreds of thousands of transactions. Emcien’s analytics quickly reveals customer buying behavior and trends that every distributor can utilize to increase sales. When suggestive selling is based on actual buying patterns, the suggestions are more plausible, and customers trust them. It is very believable when you can say “ 90% of the times this ballast is bought with this lamp and mounting.” These recommendations are sensible, simply because they are based on the actual buying behavior of your customers. Emcien’s analytics can simulate the years of experience that’s normally accumulated by working in a store for years. I need to point out that sensible recommendations are critical because irrelevant recommendations annoy customers and may reduce sales.
To illustrate the potential of suggestive selling, here are some actual numbers. ACME hardware store carries 41,155 unique items. An analysis of 232,500 orders showed that 61% of these orders would result in at least one additional recommendation. When the store implemented the suggestive selling, sales increased by 3% in the first 4 months.
Product recommendations have been used very effectively by Amazon and reported to increase sales by up to 30%. Surprised? Here is the impact to your business – Adding one more item to 10% of the orders can increase sales by 5%!
How much money are you leaving on the table because you are not leveraging suggestive selling for your products? Would you like to know?
Do Customer Buying Patterns Exist?
Customers have to make choices in order to buy configurable products. Do they make these choices at random, or are there patterns? When we look at the sales history for a configurable product, like a car or a computer, can we tell if customers have just been flipping coins and rolling dice? Or do their choices hang together and make sense? To answer this question, we would have to look at how they buy combinations of options. In the previous post, I took a pizza as a simple configurable product, and looked at how customers ordered pairs of toppings. Just by looking at the sales numbers we could detect that the selection of pineapple and Canadian bacon are not independent. Even if we had never heard of a Hawaiian Pizza, we could discover it in the data.
Even more information is hidden in combinations of three toppings at a time, or four toppings at a time. Any combination of toppings will have appeared on some of the pizzas that have been sold (or maybe none). The relative popularities of all the different combinations has a clear message: customers are not flipping coins. Some toppings naturally go together, and others do not. Pepperoni, broccoli, and anchovies is just unlikely. If a particular pizza restaurant has a few “house specials”, like the Meat Lovers and the Veggie Delight, we can see them in the data, even if we don’t know their names.
What is true of the pizza is also true of other configurable products: computers, trucks, tractors, lighting fixtures, industrial fans, and so on. All products that have variety. Customers make choices, but not by rolling dice. There are combinations that go together and combinations that do not. A pizza maker can juggle the preferences of his customers in his head. But when a product has 30 or more features, intuition is overwhelmed. The number of combinations explodes so fast that the unaided human mind can’t see the patterns. At this point, mathematical models and intense number crunching can reveal the patterns and let the product manager for a line of trucks be as confident as a pizza maker.
Do Customer Buying Patterns Exist?
Buying patterns are real, and they manifest themselves in how customers buy combinations of options. With the computing power we have available today we can detect and capture them. These patterns can then be used to design “house specials”, forecast future sales, and guide customers to what we want to sell them.
So, who else is talking about customer buying patterns?
Intel Talks about Changing technology buying patterns
Customer Buying Patterns have Changed. What’s Your Plan?
An entire report that summarizes the results of a consumer usage and purchasing pattern survey conducted in March of 2007. The survey was conducted with In-Stat’s Technology Adoption Panel (TAP) — a dynamic, online panel of more than 19,000 technology users and decision makers. Over 1,400 technology users responded to this focused survey.
Findings in this report include consumers’ time spent on PCs, when they last purchased a personal-use PC, the PC’s features/form factor/usage, the desired features of future PC purchases, changes in usage patterns, and consumers’ thoughts about new technologies.
The changing patterns include -
- When consumers are likely to make their next PC purchase.
- The features consumers state they want
- The features consumers state they really want, based on changes in their usage/buying patterns.
- How consumers view new technologies
However – buying patterns are constantly changing. As social networking grows, we are watching new markets emerge every day. There is gold for companies who can continually detect these patterns and offer the right products and feature mix.
True Demand Intelligence – Knowing who is buying what, where and why
Demand Intelligence – Knowing Who is Buying What, Where and Why
SKU numbers are an easy way to keep track of items that are built, stocked and sold. The SKU number itself is arbitrary and contains no ‘intelligence’.
The SKU number was invented for a very good reason. When this practice started, the number of SKU’s was small, and people and systems needed a simple way to track what they had. So ASP-678 may be the SKU number for a toothpaste tube with spearmint flavor, whitening, and tartar control. However, customers do not look for SKUs. They look for toothpaste with whitening, tartar control, flavors, sizes, etc. Customers buy attributes, and combinations of attributes. Some companies code the attributes into the SKUs, by concatenating two or three character codes (like SPWHTC). But this is at best a clumsy way of handling a few attributes. Companies want to know what attributes customers are looking for, and SKU numbers hide the attributes.
As the number of attributes starts to grow, whether you have them coded into the SKU number or not, the problems start to mount! The most important one is that companies do not know what customers are buying, or trying to buy. As the number of choices grows, the number of combinations grows much faster and companies drown in their own SKUs.
SKU intelligence is going behind the SKU numbers and ‘detecting what attributes customers are buying’. Knowing who is buying what, where and why is “True Demand Intelligence”.
Products have attributes. For example, a computer has a processor, a memory, and a hard drive. For each attribute there may be several alternative choices. This means that there are many different product configurations. Some companies make only a fixed subset of all the possible configurations and give each one a SKU number. Other companies allow customers to order exactly what they want, and if this is something new, then they create a new SKU number for it. In either case, a SKU number is supposed to represent a unique product configuration.
If you are trying to figure out what your customers want, then SKU numbers are a form of encryption. You have to look at your sales history in terms of the underlying attributes, and the choices for those attributes. Instead of looking at one SKU number you need to look at perhaps 20 separate attributes. The SKU number is a way of collapsing those 20 dimensions into a single dimension, with tremendous information loss. One of the things that is lost is proximity to other SKU’s, based on attribution. A customer who bought SKU A-1234 might have been satisfied with (or really looking for) SKU B-3728. These two SKUs have the same choices for 18 of the 20 attributes, and differ on only two. This is obvious when the unique configurations are represented as a set of attribute choices, but hidden when they are represented as SKU numbers. The first step in analyzing a sales history has to be expressing it in terms of the underlying attributes. Each SKU number has to be expanded into a list of choices. Then we can begin to find patterns in how the choices are made. The leather seats and the DVD player are usually bought together. Engine block heaters are not ordered on convertibles. Buying patterns exist at the attribute level, not at the SKU level.
“Buying patterns” are popular combinations of attribute choices. These can be pairs of attributes, triples of attributes, or even more. Popularity is measured by the share of sales that have that combination. Buying patterns are helpful in selling, because they reveal how customers can be moved to configurations (SKUs) that we have in stock, or that we would prefer to build. Experienced sales people are skilled at moving customers, but If these patterns are represented in some kind of knowledge base, then a computer can make the recommendations.
Customers also have attributes. The simplest is perhaps geographical location. There are patterns that involve both product attributes and customer attributes. Customers in Florida are more likely to buy convertibles; customers in North Dakota are more likely to buy engine block heaters. Customers may have several attributes, for example demographic attributes for individuals or industry attributes for companies. (We don’t assign SKU numbers to customers!) If your sales history contains information about the customer as well as information about the product, then we can look for buying patterns that are associated with certain kinds of customers.
As an example, for a desktop computer the list of attributes might be: Processor, Memory, Hard Drive, Keyboard, Monitor, Mouse, CD/DVD, Application. A specific SKU number like A-1234 is a code for a specific configuration, say (2GHz, 2GB, 120GB, Ergonomic, 22” flat panel, Wireless, R/W Combo, Gaming). The Application attribute is really a customer attribute, with values like Home, Small Business, or Entertainment, as well as Gaming. This would make it possible to look for typical Gaming configurations and typical Entertainment configurations.
SKU numbers are a useful shorthand for record keeping. Each SKU number represents a unique product configuration. But analyzing SKU numbers is like analyzing telephone numbers. To see the buying patterns, you have to go to the attribute level. The patterns exist among the attributes, so you have to decode the SKU numbers to see them.
How I want to buy a car
Every five or so years, I shop for a new car. I hate car shopping. The haggling, the long trips to dealerships way outside of town, the hours and hours of waiting, punctuated by furtive whispers to my husband, “Don’t give in! Stick to our budget! But don’t tell them our budget!” and similar. But that’s toward the end of the process. There’s a lot of work leading up to it.
First I hit the Consumer Reports site to research cars. A subscription is just $5.95 a month, but it auto-renews so you have to remember to unsubscribe or it quietly chips away at your wallet forever.
I find the five safest vehicles according to my car type and year. When I say new car, I just mean it’s new to me. I like to benefit from someone else’s new-car depreciation, which is something like 25% the minute you drive off the lot.
Anyway, I get on several different car sites like CarsDirect.com and AutoTrader.com to look for my next set of wheels. First I have to pick make and model, then enter my ZIP Code, then there’s a long list of cars. If I want to, I can see the list from lowest price to highest. The trouble is, I want to compare five different models and several different years. I’ve got to select the same filters over and over for all five and then compare the info. continue reading »
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After the recession, thinking leaner
Where will we be after the recessionary cloud lifts? Working even harder to add value to products and services while keeping costs down. See Chris Chiappinelli’s recent post: A New Era of Thrift.
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Q&A with John Sloan, former director, Jeep Brand Global Product Marketing
In today’s post, John Sloan talks about challenges dealers face in ordering inventory that best matches customer demand.
Emcien: Describe the Chrysler-Emcien initiative that examined dealers’ struggles with complexity in the ordering process.
JS: In a soft “push” market where volume is driven by heavy incentives versus the merits of the brand / model, managing cost is paramount. A key piece to focus on is product inventory. Dealers get roughly 60 days of no-interest floor plan. In a soft market, vehicles can easily sit for longer than two months before being sold, so it’s critical that vehicles be easy to order, stock and sell. Simple is better.
Emcien worked on a model to simplify the Chrysler PT Cruiser product mix. There were thousands of possible build configurations for the PT Cruiser, creating significant complexity for engineering and the assembly plant, as well as the supplier extended enterprise. Emcien’s ability to accurately forecast demand is invaluable for a complicated product line because it can assist with reducing the build configurations to those that best match demand. The PT Cruiser initiative validated the power of the Emcien inventory model.
Q&A with Mark Gottfredson, Bain & Company
In today’s post, we talk to Mark Gottfredson about product complexity and customer choice.
Emcien: It’s natural for companies to add products and features to keep customers happy. What are the downfalls?
MG: The challenge of adding complexity is it’s the most natural thing in the world. Marketing comes up with new ideas for products or configurations to get the next bit of market share or a little bit more share of wallet. But most companies aren’t so good at retiring products; they don’t have a similarly robust process for taking things out of the catalog that no longer sell, or sell only small amounts. They don’t do a good job of balancing.
Most decisions we make are based on incremental economics. Each decision makes sense in its own right, but the costs of complexity tend to grow systemically. You can’t tie them to a single product decision. Take tinted windshields, for example, that you can sell as an option for $120 and 40% of customers will buy. Assuming the costs of tinting the windshield including inventory impacts, etc., are $9, it will always make sense to add the option. By itself, it is a rational decision, but when coupled with hundreds of other decisions, we end up with dozens of options like power windows, 13 exterior colors, 10 interior colors, 7 different radio and speaker combinations, etc. Eventually, the vehicle can be made in 10 billion different ways, and you don’t know what the next order will be. Since you can’t effectively forecast anymore, you get frustrated and buy a $50 million forecasting module to try to manage all the complexity. You have difficulty balancing your lines, build inventory and increase supply chain costs. Unfortunately, when most companies finally decide to reduce complexity, they “cut off the tail” of low-running options or SKUs. But they don’t remove the systemic costs, and they don’t see any benefits.
Emcien: Companies often overestimate the value buyers place on having many choices. What are the downsides?
MG: Go to a banking website like Citibank or Bank of America. The site describes itself as a full-service bank that has all the items you could want. There are long lists of products like credit cards with different reward programs, as if to say, “We have a lot of products. Surely there’s one here for you. Good luck finding it.” High complexity is a priori evidence that you don’t know what your customers want.
Emcien: When do fewer choices mean higher sales?
MG: When you understand customers. Dell understands customers well. Dell’s website is Spartan; there are just a few choices. If you choose a desktop, up pops three computers: high, medium and low cost. These three configurations are what your segment – home, professional, government – wants. You can customize each one, but you’ll make it as expensive as the next higher model, so then you switch to that and you’re still buying a standard configuration. Every time I have seen complexity reduction done right, sales have increased.
Emcien: How do overoptimistic sales expectations help to spread complexity?
MG: What happens is sales looks for a gimmick that gets them the next sale. Many manufacturers think whatever’s thrown over the wall from product management and sales must be good to go. And sales thinks more is better! Engineers love to engineer; they’ll give you complexity. Most firms build complexity systematically into operations, and then they build systems to handle the complexity, and that’s high cost.
Companies should think about what business would be like with a zero-complexity baseline – how they would operate if they offered just one product or service. The purpose of zero-based thinking isn’t to eradicate complexity; it’s an exercise to reimagine the business with the optimum amount of complexity.
Mark Gottfredson is a director of Bain & Company’s office in Dallas, Texas, which he founded in 1990. Over the past 26 years, he has advised chief executives and top-level managers in a wide range of industries. Currently, he serves as the Global Head of Bain’s Performance Improvement Practice and is also a leader in the firm’s business strategy, airline, financial services, manufacturing and energy practices.
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Arm your salespeople to make the sale
I was talking to an executive at Oracle, and he told me that CRM is entering a new phase. Salespeople are the revenue generators of a company. Current CRM tools have served the purpose of helping salespeople organize their customers’ contacts and manage the sales process and pipeline, but this isn’t enough.
Your salespeople are representing and selling your product. Customers who want to buy your product typically list a few things they want and look to the salesperson to guide them. The salesperson is their advisor on your product offering. The salesperson is expected to know the product and suggest good choices for the customer. Is your salesperson equipped to do that?
There was a time when life was simpler and products were simpler. The customer said, “I want a 17″ TV.” The salesperson could look at what he had stocked and reply, “I have a 19″ I can give you for the same price.” Wow! Done!
Today, even the best salespeople don’t stay at one job for long. They move, selling what sells. Training sales newbies on a product is a big challenge for companies, and the cost of the salesperson not knowing the product he’s selling is VERY HIGH. As many as four out of five quotes are lost because customers weren’t guided to a good product selection. You can fill this gap by arming your salespeople with tools and product knowledge that will help them advise customers effectively on your product. Your company needs salespeople to have that capability so you can make money on the stuff they sell!
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Guiding salespeople
We have talked a lot about how configurations and complexity affect an organization, but often we forget to look at customer-facing roles. While managing product complexity is important for product teams and production teams, it should also extend to the sales force.
At the end of the day, the number one mission for your sales team is to SELL. And often this push for revenue brings additional complexity back into the organization through new one-off configurations salespeople have promised to customers. Even worse is that these configurations might be one or two small changes away from a very popular and maybe more profitable configuration.
Product configurations can be used to shape not only customer demand but also sales behavior. Using a set of pre-ranked configurations based on metrics such as margin, days to sell or current inventory level, you can offer your sales team a structured plan that incents sales through tiered commissions.
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What is sales history, exactly?
We often talk about the sales history of a product, so let’s explain exactly what it means. There is a raw sales history and a collapsed sales history. The sales history, raw or collapsed, is the starting point for all the analytics we will be introducing later.
Raw sales history
A product is a collection of features, where each feature has a set of mutually exclusive options (one of which may be “no,” “none” or “none of the above”). A sales history consists of a record for each unit of the product that has been sold, with a list of the options that were included. Since each record is for a specific unit, there may be a serial number feature. So imagine a table with a row for each unit sold and a column for each feature. The entries in a column are the different option choices for the corresponding feature. Blank cells indicate a “none” choice.
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