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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!
Follow the Money!
Buying patterns and the economy are constantly changing. Some products and categories that were popular are not anymore. You cannot control your customers’ tastes or the economy. But if you follow how the money is being spent, you can make a lot more! Unlike clicks and page views, buying patterns are very reliable as they are based on actual sales. Money changed hands. An economic transaction occurred!
Track sales transactions to understand your customer’s buying patterns, establish a more relevant product mix, satisfy more people and sell more.
Your customers speak to you when they buy. If you can listen to what your customer wants you can manage the buying process and you can influence and even control it. “Why would I want to do that?” you may ask. By better understanding your customer buying patterns you can establish a more relevant product mix that will satisfy more people. You can also guide them to more profitable choices at point of sale based on product availability or close substitution. You will satisfy more people and sell more. You will also make it easy for them to buy your products and services.
The Analytics of Buying Patterns
First, take the guessing out of the equation. You need to know what your customers are purchasing and what they want to buy from you in the future. This intelligence is available in your sales transaction data. Customers buy your products and services in distinct patterns.
Products and services have become more complex and companies offer a dizzying array of choices. However, with analytics the sales data will reveal popular combinations of choices. These popular combinations are guides on how you can make your products and services easier to buy. How you can make is easier for customers to do business with you.
There is also the issue of product profitability. Some of the choice combinations are more profitable than other. Again the analytics will reveal which combinations are moneymakers, and which ones not! Once again – if you have access to this intelligence, you can stock the right product mix and guide customer to better choices. If you stock inventory in your store you can leverage this intelligence to plan an optimal inventory mix. That means making the most money from the least amount of inventory investment while satisfying your customers’ needs.
Whether you are running an online store or a brick ‘n mortar store – this is a key principle to selling more and maximizing your capital utilization.
Demand Sensing And Demand Shaping
Forecasting and planning is a challenge in the best of times. The times we are in make this a herculean task. Market demand shifts continually as economic conditions change, products change, prices fluctuate, competitors act, new products are introduced, marketing creates promotions,……. The list is quite endless. Current planning and forecasting methods are reactive and struggle to keep up with these shifts.
The solution is “Demand sensing and Demand Shaping” – active methods to predict what demand will arise and keep ahead of the market. Demand sensing is the ability to detect what choices customers are buying patterns and the trends associated with these choices. Demand sensing can help you to quickly see market shifts to plan your product mix and offering.
Customer Buying Patterns "Customers who bought this SKU also bought this other SKU"
Demand shaping is the ability to guide customers to the best choices at point-of-sale. This is the key to increase revenue and supply chain efficiency. However, demand shaping needs product intelligence at point-of-sale to guide customers to the best choices. Some of the ways to demand shape are –
- If you offer many products or SKUs, there are typically strong buying patterns in the demand. For example – This printer is often bought with this unbleached paper, this ink cartridge and cable. Then, when a customer selects the printer at point of sale, you want to automatically show him the other items that have strong buying patterns. The customer will thank you for this recommendation because usually they need this additional stuff, and you just saved him a ton of effort thinking about it, and a ton of time searching for it. And you made more money in this sale!
- If you offer a product with many attributes, every sale will begin with the customer calling out a few attributes. The opportunity to demand shape is to recommend a good choice based on the partial list of attributes the customer has called out. Demand Shaping requires the ability to complete the order with the right attributes. The best way to complete the order is to have sales intelligence these attributes are bought with these other attributes. It is the Amazon-esque way to look at products with many attributes.
- The biggest opportunity of Demand Shaping is guiding customers to close-enough SKUs. Most customers describe the products they want to buy with a ‘kinda-sorta’ attribute description. As the number of product features grow, there are a large number of SKUs that are similar or close-enough that they can satisfy the customer. So there is a significant opportunity to guide a customer to a similar or close-enough SKU at the point of sale. The recommended SKU may differ in attributes that the customer did not “call out” or specify. If you can offer up this SKU it is a win-win. You have served the customer. You have won the sale. You have moved your inventory. And your competitor did not get this customer.
As product choices and the number of SKUs grow, these techniques are mandatory for an efficient supply chain and for a good customer experience in this customer-centric world.
I just read an article by Mark Pearson, Six secrets of Supply Chain Planning Masters.
Quoting Mark Pearson’s article – Think of demand sensing as predicting what demand will arise, as opposed to simply reacting to incoming orders. Shaping demand, on the other hand, is all about steering customers toward available products and services. Compared to laggards, more than four times as many masters said they can predict demand with greater than 80 percent accuracy levels. And nearly twice as many masters said their ability to shape demand was “good” or “excellent.
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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.