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Increase Sales With Analytics on Sales Receipts
Increase Sales with Purposeful Analytics On Sales Receipts
The sales receipt is a neatly itemized list of purchases. Every purchase comes with a specific need, and hence the sales receipt is the true voice of the customer. As demand patterns change, the sales receipt data can reveal tremendous intelligence on what customers are buying, the changing trends and what the future purchases will be. “Stores Face New Kind of Shopper” is a very interesting article by Ann Zimmerman and Rachel Dodes in The Wall Street Journal (Monday, December 28th 2009).
The financial crisis has dramatically impacted sales in all markets. Over the last two years sales have plummeted, consumers have disappeared and profits have evaporated. The financial crisis has caught us in a time of tremendous over capacity. In the B2B markets, companies have been dramatically shrinking capacity to match the new level of demand. In B2C markets, retail experts generally believe that the US now has more stores than consumer demand can support.
Customer buying patterns are dramatically changing as capacity adjusts to the new level of demand. The financial downturn further impacts this change, as customers look for new ways to stretch their money. To complicate things further, customers today have many choices of products, channels and price point. The internet has become a primary source for browsing and comparison shopping. This extends the reach of the customers, and puts pressure on companies to cater to wider product choice selection. As these shifts continue to change buying behavior, companies must have the capabilities to stay ahead of the changes. With the speed of change in products, companies need to adapt fast and stay in tune with changing demand.
The good news is that the sales receipts reveal these changing trends and buying patterns. However, this requires purposeful analytics designed to convert sales data into actionable tasks. I would also like to mention that sales data has a unique structure and characteristics. The purpose of the analytics is to reverse engineer the sales data to determine what is selling. If your product has a lot of feature choices, you can get insight into the popular choice combinations. If you sell lots of individual items (i.e. large number of SKU’s), you can get insight into what are items that are commonly grouped together. Emcien offers analytics designed for sales data. Emcien’s advanced analytics cal also give you intelligence into what choices cause the selection of other choices. Armed with this insight, you can manage your product offering to always stay ahead of the trends.
With purposeful analytics designed for sales data, you can get insight into -
- What product choice combinations are popular?
- How do the choice combinations vary by channel?
- What choice combinations are profitable?
- What are the changing trends and what choices will sell in the future?
As the market shift continues, this level of demand intelligence is mandatory to stay profitable!
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.
Self-Service simplifies Product Offerings and increases Margins
Self service is a term we all know, such as pay-at-the-pump gas and self-checkout stations at some grocery stores, and now more obscure things like video game kiosks by GameFly, but the true tidal wave of self-service hasn’t even started, and it’s going to be good for both the consumer and the manufacturer, if done right.
Self Service Grocery Scanner
When you checkout your soda and cereal by swiping products across a scanner at the auto-checkout stations, there isn’t much complexity other than when you get a problem with the scanner reading a smudged bar code or trying to locate the button for ‘snap beans’ when you put those on the scale. The transaction is smooth, quick and you are in control, which is a good feeling as a buyer, you are not being sold, you are buying just what you want, quickly and easily.
But what happens if you try to buy a “configurable product“? In the grocery store, the only thing configurable is the weight of produce, but other than that, the costs and configurations are set in stone and are detected by reading the bar codes. Easy to understand as the buyer and relatively easy to deal with as the seller. Configurable products are those where you have to make many choices before you can order the one product. Products like computers, cars and thousands of others where the buyer has to describe their preferences or choices so the product can be created and delivered. It’s even more complex in a B2B environment than it is in B2C, where the products available and choices are astronomical. Products like Lighting, Valves, Agriculture and Construction Equipment, Lifts, Electrical equipment, cooking equipment and conveyors have more choices and variants than you can imagine and that variety makes it hard to order, build and deliver efficiently.
Usually a large direct sales force is sent out with complex price books (sometimes online in PDF form) to sit with customers and prospects and help them combine choices in hopefully valid ways. The choices a customer have to make are quite extensive, ranging from tens to hundreds of choices. Most of these choices the customer doesn’t care about, but they are required by the manufacturer just so they can build a valid product. Customers care about the few things that matter to them but after that, they will just choose things that “seem to make sense” just to complete the order. Sometimes they don’t even do that, they get so frustrated with 60 more questions about features and options on the product (many of which they don’t understand) that they walk away.
In some cases companies believe that putting in a configurator is the solution to their problem. Configurator’s automate the order process by ensuring that the order is VALID. The engineering and marketing rules that drive what can be built and offered are setup in a configurator such that the user ordering the product is led through valid questions and end up with a build-able product. Now this product may be build-able but it also may be a one-off low-margin brand new SKU that manufacturing hasn’t built before and requires some parts they aren’t carrying at this time. All this for something that was only 2 choices from a very popular configuration. And those 2 differences only happened because the customer was asked 20 more questions after they entered the 5 things they cared about. They chose as best they could, but without any guidance or suggestions, ended up on a new SKU which will ultimately explode into huge numbers of parts and processes to support the new SKU.
Now if the customer only had to enter the 5 things they cared about and the system recommended the combination of other choices such that the customer’s price limit was met and the configuration wasn’t a new SKU and the SKU had a good margin, then it would have been a win-win for everyone. And the whole process could be complete quickly and easily. The customer wouldn’t have to answer any other questions and would feel that same feeling that you do when you swipe your can of soup across the scanner at the market. The manufacturer wins as well because the customer was guided toward an existing configuration so the cost of creating and supporting a new SKU was avoided. It’s happening now with recommendation engines that leverage buying patterns to suggest full configurations based on the few attributes a customer gives it. Just like Amazon can recommend other books you might want to read based on the current “fly fishing” book you are looking at now, suggestion engines can be utilized to provide this convenience for much more complex products.
That’s the self-service tidal wave that’s coming, when all products, not matter how complicated can be ordered by simply asking for the attributes that YOU care about, what your price limit is and then Voila! it’s done. Customers will order more from companies that offer this convenience. Just think about how often you walk into the gas station to pay as opposed to pay at the pump. And if you had two stations to fill up at, one was pay at the pump and the other required you stand in line after pumping the gas, which do you think you would most often go to? Simplification is good for everyone, and profitable too.
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The quality connection
Recently I wrote about quality rankings for automotive manufacturers and the perception of these rankings in the market. While the marketing teams at these companies must shoulder the burden to convince consumers about their products’ quality, there is a very real connection between product quality and configuration management.
In many industries where products have grown over time with constant additions of new features and flexibility to allow customers to build to order, the level of complexity is staggering. Often the number of configurations sold on an annual basis is surprisingly close to the total units sold for that same period. This “snowflake” situation is one of the worst possible scenarios in product complexity as each unit has its own signature. Obviously, the production of these products also requires flexibility in manufacturing. This may result in reduced use of automation, and often it leads to units being reconfigured where components installed during one step are either removed or modified in a later step due to a unique situation.
These one-off manufacturing processes open the door for product quality issues due to fewer controls during production. Put simply, if I can reduce the number of different things that must be done during production I should be able to do those things better.
So product management teams have direct input on product quality via product complexity. Managing the product option mix to reduce the overall number of configurations can promote the increased quality that all manufacturers are looking for.
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Stop product complexity at the door
In any manufacturing company that builds configurable products, there is a lot of discussion around what product complexity is. What’s interesting is that when times are good and there are lots of sales, the discussion is usually around how to simplify or streamline with the goal to sell more product even faster, that complexity is keeping sales from going even higher. In bad times, the discussion typically moves to how complexity is causing undue stress on the supply chain, creating problems with parts forecasting, quality and finished goods inventory.
Rarely do these discussions end with participants really agreeing about exactly what complexity is or how to reduce it. Solutions are attempted with internal projects like SKU reduction and part number reduction initiatives driven by Six Sigma teams that mean well and do good work, but usually are chasing the tail of the complexity dog, rather than leashing it for good and guiding it to higher profits, lower forecasting errors, even shorter sales cycles.
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Optimization is the big win – but getting started is key
When I started studying complexity and realized the huge adverse impact it was having on companies, I was determined to “find it and get rid of it.” There are many places where that formula will lead to big improvements in everything – profits, service, quality and more. More and more companies are discovering how to do this. In some cases it is pretty simple. Just having the courage of their convictions that it will make things better is all that stands in the way of eliminating complexity.
Well, I found that is not completely true – at least not all the time. There are some situations where what seems to be a simple complexity elimination process turns out to be quite a bit more… complex! The real issue is not just complexity reduction. It is “optimization” of complexity. Get rid of the wasteful part and structure processes to use the right level of complexity.
The Root Cause of Product Complexity!
Emcien defines product complexity as simply the ability to predict what the next order coming into the company will be.
Think about it: If you only made product configuration A, you have 100% confidence in knowing that the next order in the door will be configuration A (assuming you get an order in the door at all, not a total given in this economy). But if you have configurations A and B, it’s harder to know and with A, B and C, it’s even harder, and so on. When you have thousands of configurations, predicting the next one is very difficult.
It’s not just the number of configurations that’s important but also how they’re distributed. If I have 10 configurations but 90% of my orders are for config A, then it’s still safe to predict that the next order is config A. But having 10 configs that have each been ordered 10% of the time is extremely complex!
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