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“This Sells with that” for Aftermarket Parts
The SKUs (stock keeping units) in an aftermarket business is nothing but the various components of an automobile, tractor or backhoe. Every new model that comes into the market sees an addition of 2,500-3,000 parts to the existing master list, while the addition of every variant adds another 500-1,500 parts. The last forty years has seen a phenomenal growth in the number of aftermarket parts. This phenomenal growth has been fuelled by the liberalization of global players resulting in thousands of models of cars, tractors and other machines.
Frequent launch of new models has reduced product life cycle and has negative implication on the aftermarket supply chain. The implication of shorter life cycle of automobiles on the aftermarket business is faster transition of parts from runners to repeaters and soon fading into obsolescence. The revenue from parts of a new model are typically low in the first two years after launch, increasing in the third and fourth years. The sales only stabilize if the model continues in the market. Unlike other businesses where a product is discontinued if it does not yield revenue, in an aftermarket parts business the part needs to be supplied even if the model has failed. This tremendous volatility impacts the number of parts the aftermarket business has to support.
For example, if an OEM introduces a new model and two variants each year – this results in about 3,000 parts. In six years time the parts proliferation in the aftermarket business of this OEM will be least 30,000 parts. This explains the relevance of having a SKU management for the aftermarket business in order to track the movement of parts till it reaches obsolescence.
Levering “this sells with that” Intelligence from Invoices
The challenges due to inventory and assortment planning for the aftermarket parts are significant. Traditional forecasting methods that treat each part as an independent entity are outdated. For aftermarket parts, the performance of traditional forecasting methods is dismal, and hence not all the parts can be forecasted. In fact, only five percent of the master list can be forecasted with an accuracy of 75 percent on a monthly basis. This results in bloated inventory, incorrect parts assortment and lost sales.
OEMs, distributors and retailer have realized the importance and complexities of the spares business and have initiated measures for assortment planning and SKU management. This requires sustainable methods as the market grows and competition gets fiercer. Every OEM has mentioned how the downturn has reflected in increased sales for the aftermarket/service parts. A part of the business that was typically ‘a nice to have’ has become a focus for improved customer service and profit.
"This Sells With That" For Aftermarket Parts
The aftermarket business deals with SKUs in tens of thousands. Unlike soft goods, in this business customers buy parts for projects and jobs. The invoices reflect that, and are comprised of parts bought by maintenance and repair shops to get a job done. Hence, the affinities in the sales transactions reveal what parts that are bought together for repairs/jobs.
The sales transaction data for the aftermarket businesses is very rich with parts affinities and trends. That’s because the data reflects the buying patterns of repair shops and mechanics. This represents a significant opportunity to leverage the transaction patterns for assortment planning, inventory management and of course, ‘suggestive selling’.
Emcien offers analytics that reveals patterns in sales transactions, producing a complete data map of the item affinities for ALL parts. This intelligence can be input into traditional warehouse planning and retail assortment planning systems. The affinities data can be used to make traditional systems smarter as the sales patterns change, parts change and market shifts.
The current sales patterns and item affinities in planning systems are input manually using manufacturer recommendations and gut feel. These recommendations get stale very quickly and are rarely based on actual sales invoices. The value of analytics driven affinities is that the relationships are always up-to-date, backed by actual sales transactions. Emcien’s analytics produces affinities data for all parts in the supply chain eliminating the need for ‘hard coded’ rules that quickly become obsolete and are a nightmare to maintain. This enables aligning parts inventory with with sales/model changes/parts utilization based on actual invoices. The affinities data can also be use for suggestive selling to increase customer service and order size.
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Revealing Patterns of Change
This is Fun, But Not When You Are Under the Gun!!!
Gartner has launched a new focus area called “Pattern Based Strategy”, based on the need of businesses to capitalize on large amounts of data and the new rules for business process adaptation.
Here is a great verbatim quote from the Gartner web page.
The depth of the recent recession blindsided most businesses. As the economy starts to recover, many business leaders are thinking, “If I had seen this coming sooner, I could have acted faster, decreased my risk and enhanced my opportunities for growth.” There is a way to see things coming. It’s a framework for proactively seeking and acting on the early and often-termed “weak” signals forming patterns in the marketplace. It’s also about the ability to model the impact of patterns on your organization and identify the disciplines and technologies that help you consistently adapt. It’s called Pattern-Based Strategy.
The key to Pattern Based Strategy is automatically revealing intelligence that is hidden in the data/information. Companies today are running more lean than ever before. Employees across all organizations are inundated with work and overloaded with data. . There is a great need for technology that will make our jobs easier and make us more productive. At Gartner, the idea that emerged, led by Yvonne Genovese, is called Pattern-based Strategy (PBS).
We are victims of too much information, missed opportunities and ‘@#$% I wish I could have seen that!‘ moments. Connecting this to a rather timely/charged topic – Think about a recent attempted terrorist attack by the Nigerian traveler who bought a one-way ticket, paid in cash, checked no bags, boarded an international plane. There were a very large number of ‘red flags’ in the sequence of events, and there was a large volume of data hiding all this intelligence. A Hope Strategy is to hire tons of people and make them search the data for red flags, more importantly sequences of red flags. This may work sometimes. But it is a poor and expensive strategy, and rarely does it produce the desired results on time! (making it quite useless, actually!)
As companies start to incorporate intelligence from data into their operations, one of the primary issues is the ability to have the intelligence automatically come to you. ‘Digging for insight’ is a poor, time consuming, expensive strategy. We need the technology to work for us. Second, it is also important to start focusing the insight with a particular business function/strategy in mind. Sales, Marketing, Operations, etc.
Connecting this back to what we do, Emcien provides analytics that automatically reveal customer buying patterns in sales data. The analytics reveals the popular choice combinations, key differences by region, key trends and new emerging segments. This is an example of technology working for you, bringing insights back so that you can act on it.
Quoting a Regional Practice Manager and the Senior Architect for Siebel -
Emcien offers rigorous and repeatable detection of buying patterns, enabling your customers to act on them, while supporting your product objectives (margin, inventory, velocity, …)
Quoting a former Oracle Practice Manager and Senior Siebel Architect -
Emcien offers rigorous and repeatable detection of buying patterns, enabling your customers to act on them, while supporting your product objectives (margin, inventory, velocity, …). Emcien’s offerings readily integrate with Siebel, enabling immediate improvements to revenues. Few projects offer such potential for improving the customer experience and increasing revenues, with so relatively little development or integration efforts.
Automatically revealing patterns is required today as we all drown in data, and do not have time to hope that someone may find the intelligence that the organization needs to act on. Thanks to Gartner for launching this focus area!
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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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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.