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SKU Rationalization Demands Market Basket Analysis (aka Customer Buying Patterns)
SKU Rationalization Demands Market Basket Analysis (a.k.a Customer Buying Patterns)
Wal-Mart Stores Inc., the world’s biggest retailer, is bringing back some products it had removed from shelves last year as shoppers turn to competitors for a wider selection of merchandise. A failed SKU rationalization effort?
The company met with suppliers about reinstating items to keep customers from going to other stores, said Leon Nicholas, a director at consulting firm Kantar Retail who has spoken with manufacturers about the move.
Wal-Mart is telling suppliers it cut too much in some areas and wants to bring some items back, Smith said. The retailer is noticing that consumers are visiting other stores and no longer going to Wal-Mart for everything they buy, he said.
“I’m learning this from my suppliers who were down to one SKU in the store,” said Smith, who helps vendors hire account managers and other representatives to call on Wal-Mart merchants. “Now they’ve got a seat back at the table.”
Sales at Wal-Mart’s U.S. stores open at least a year declined 1.6 percent in the fourth quarter, more than its forecast of a sales decline of no more than 1 percent. Declining store traffic reflected disruptions caused by store remodeling, Wal-Mart Chief Financial Officer Tom Schoewe said last month.
Why did Walmart’s SKU rationalization effort fail? Because Walmart ignored the market basket effect. It is not an issue of cutting too many SKUs; it is an issue of cutting the wrong SKUs because you do not know the product associations in buying patterns. A low frequency items can be profitable and may be often bought with other low frequency items. If you cut one of these SKUs, you will lose the customer. On the other hand, there are SKUs that are bought in low frequency in 1-item baskets. The loners! These are typically low margin, high capital utilization SKUs. These SKUs can be easily identified with Customer Buying Patterns Analytics.
What most retailers ignore in SKU rationalization is the market basket effect. Profitable customers may take their entire basket elsewhere, if they can’t find certain items (even if those items are “slow-moving”). The market basket analysis across hundreds of thousands of SKUs requires advanced analytics. Based on testimonies from Wal-mart customers, people were in fact choosing to go elsewhere for many of their shopping trips. This is why Wal-Mart has changed their tune very quickly.
“They are calling me back and saying, ‘We need to hire somebody who has experience in this category and knows this buyer — it looks like we are back in business,’” said Smith, who is based in Rogers, Arkansas. I think Walmart does not get it. They need help not just in categories; They need help across the categories. They need help on what items are typically in a basket – also called Customer Buying Patterns!
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What is Pattern Based Analytics?
Emcien's Pattern Based Analytics Automatically Reveals Choice Combinations and Trends in Sales Transactions
A fast emerging area of business analytics is Pattern Based Analytics (PBA). This has been launched due to the very large amounts of data and need for analytics that can reveal meaningful patterns that businesses can act on. A typical reaction to the large amount of data is “If I had seen this coming sooner, I could have acted faster, decreased my risk and enhanced my opportunities for growth. Pattern Based Analytics typically requires focus on a business areas, e.g. Sales, Marketing, Finance, etc. The key to Pattern Based Analytics is automatically revealing intelligence that is hidden in the data/information.
This is a fast growing area because of key value points:
Instant Use - The inherent nature of Pattern Based analytics is that it does not require models and it accepts unstructured data. Hence, one of the greatest value points is Instant Use!
Accepts unstructured data – A key value point that drives down implementation time, barriers and cost, and dramatically increases applicability of the analytics. The ability to detect patterns in unstructured data makes it very easy for applications from sales data, marketing data, to twitter strings.
Big Problems are easy – Problem size and data size are not an issue with PBA. On sales data, Emcien’s PBA will easily solve buying patterns on 250,000 to 500,000 SKUs in a few minutes. This offers the ability to solve problems that were too large/expensive to solve previously. This is a game changer, when the closest alternate solution requires complex models and has serious size limitations of a few hundred SKUs.
Works on problems big and small – On problems big and small, PBA is a natural fit. PBA dramatically lowers the price of analytics, enabling smaller companies to gain immediate value from business analytics.
No data-models, No data-cube, No set-up – This is one of the single biggest value points for PBA. This eliminates the need for specialized analysts, statisticians and technical staff to interact and maintain the system. The ability to accept unstructured data and not require a model means No Setup. This also means you can go live now! No more 18-month implementation cycles!!!
Intuitive for non-technical users – Pattern Based Analytics can present results naturally in a very intuitive way. This is because the patterns that are pop are typically the top categories that need attention. There is not need to drill down and ask questions – the ultimate bain of every BI user.
When Pattern Based analytics is pointed at sales data, the patterns that pop are “what are the top selling items”, “what is the pattern of choices combination”, “where is this happening”? Any non-technical business user can use this report to stock better and drive more sales.
Always up to date – Patten Based Analytics does not use models and cubes. Hence there are no cubes to maintain and update. Even as time passes, the analytics are always up to date, due to the ability to input non-structured data.
Gartner has rightfully established Pattern Based Strategy as the next frontier for capitalizing on large volumes of data and deriving value fast and continually.
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A Race Towards Pervasive Analytics
Gartner’s top 10 trends for 2010 set the stage for Cloud computing and Analytics.
Analytics is context driven, and presents actionable results to the business user. BI allows the user to slice and dice data. BI is good if you know what you are looking for. The reason Gartner placed analytics above BI is because of the needs of businesses today to act on data, as opposed to merely having access to it. There is way too much data. We do not need systems that create more data – we need intelligence from the data, which is what Analytics does. Hence the positioning of Analytics on the Gartner charts.
BI has become pervasive, as it should be. It has even entered open source with Pentaho and Jaspersoft, a sure sign of being pervasive! However, this was inevitable as every business user needs easy access to their data. A recent survey conducted by B Eye Network involving more than 1,000 respondents from around the globe found that only 12 percent said they had no plans to use open source software in some form for business intelligence applications or data warehouses.
However – converting the data to intelligence, and actionable intelligence in the next frontier. That is why Gartner placed Analytics in their top 10 trends chart, and moved BI out! As we have watched with Google analytics, the analytics on web traffic data is pervasive. There are a myriad of products that provide analytics on web stats, but Google provides a universal product ensuring that everyone has access to it.
Analytics on corporate data will also become pervasive. Companies are demanding this. The analytics will be contextual, as this is required for analytics to automatically make sense out of data. The analytics will be agile and companies will be able to pour their data in, and watch the results take shape. Much like Google analytics on web traffic data.
Emcien’s analytics offers analytics on sales data. The context is sales and customer buying patterns. Companies can now pour the sales data and watch the customer buying patterns emerge. No data mapping and model building. No long implementation cycles! The ability to “just turn on and use” is key to being pervasive.
The future is here!
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Demand Intelligence Among IDC’s Top 10 Retail Predictions
Just read a great article by Amanda Ferrante based on an interview of Leslie Hand, Research Director at IDC Retail Insights for Retail Touchpoints. The interview focused on providing new strategies to optimize the value of IT in 2010, as IDC Retail Insights unveiled its Top 10 Predictions for the retail industry. The IDC report explored several hot retail topics, including social commerce, mobility and how demand intelligence is driving inventory management.
In the interview for Retail TouchPoints Leslie expanded on the IDC predictions. The first set of points were around the impact of social networks and it sizeable impact in retail. Everyone is talking about social networks, and we all know it is like the iceberg that sank the Titanic. We see a small piece and there is a big chunk underwater! (bad analogy!) But no ones knows the true financial value of this. I guess the analysts counsel is to chase this bubble it and stay on top of it, so that when folks figure out how to monetize it, you are positioned well. Just hope that we are not standing there with a load of tulip bulbs!
The interview mentions Demand Intelligence as being critical. Leslie says that retailers with capabilities for sensing demand were able to fine tune assortments, reduce demand forecasts and adjust prices and promotional programs to maintain margin expectations given expected product sell-through issues. On the flip side, many retailers were left holding the bag, as they did NOT have the capabilities to adjust to the changes. As consumer spending shifts and demand fluctuations grow, Demand Intelligence is mandatory to run a a profitable business.
In Leslie’s words “Many of the retailers who were not able to adjust expectations soon enough, because of the timeliness of demand data or insufficient analytics, remedied the situation by investing in better forecasting and planning tools in 2009. Part of the success this past holiday season can be attributed to merchants sticking to the plan, with an understanding that the data and the analytics and planning tools that were used to develop the plan were smarter and more agile than human experience alone. We believe this was a tipping point for many retailers, who may not have been fully invested in demand intelligence before.”
The predictions also touch on the importance and trends of mobile applications, and connecting customer behavior and purchase data for more insight. However, to me, the second one sounds like lots of data, long implementations, ……
On that note, I would like to add that a key factor for applications to be successful in this economy is quick proof points and fast implementation. As competition heats up even more, retailers needs tools that can quickly demonstrate proof points and deliver value. Solution providers and software vendors need the capability to quickly implement their solutions, without lengthy and costly implementation cycles. This is even more important for the small and medium retailers. I guess the bigger guys would say … why leave me out! :))
[J86ZEDNBT7UH]
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Making Analytics Actionable
I just read an article titled “Making Analytics Actionable” by Michael Vizard. He makes two good points – predictive analytics is not new and analytics needs to be actionable. I could not agree more.
On the first – predictive analytics has been around for a long time. We used to call it forecasting. It was difficult then, and it is difficult now. Forecasting gurus, or should I say Predictive Analytics gurus have thrown every mathematical trick at the data to predict the future. It reminds of a quote by the CEO of a Fortune 500 company, who said “No one knows how to forecast. If they did, they would be in a different business.” I think by different business he meant forecasting money on Wall Street. But we all know that has gone! And now may be the quants on Wall Street would agree with him as well.
On the second point – yes, analytics should give you actionable information. As I hear from our customers, time and time again, they do not need more data. Companies are drowning downing in data. (maybe “downing” is the right word!!! We are downing in data! It is a downer!!!) In the name of Business Intelligence, they now have the capability to slice and dice this data, creating more data! The purpose of analytics is to convert all that data into something meaningful and actionable. If the analytics does not accomplish that, it is just another BI tool.
As you investigate analytics for your business, here are a few best practices:
- The analytics need to be focused by business function.
- The analytics needs to answer the question “What do I do with this?” and “what is the business value”.
- The analytics should make your job easier, and the recommendations should want you coming back to it over and over again.
The answer to the question “What do I do with this?” should be actionable tasks that a business should be able to run with. That is called Business Analytics. The reason the third point exists is because you, the user, would only come back to it over and over again if it made your job easier. That is the key to business analytics. “Take all that data and convert it into actionable tasks and make your job easier”. How is that for a tag line!
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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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How Good Is Your Demand Intelligence?
I was meeting with Martin, the CEO of a fortune 1000 Company. He talked about sales productivity as a significant opportunity and an area of strategic focus for 2010. “We have been selling this product for years. Yet, every day is ground hog’s day for our sales reps. What I mean by that is every sales call and quote request is treated as if we have never sold this product before!”.
Maximize Sales Productivity On Every Channel With Demand Intelligence
In the B2B markets there are distinctive patterns in the product choices that customers make. It isn’t really customer intelligence as much as demand intelligence. The B2B markets are different from B2C. The average purchase value is typically larger, the frequency of the same end-customer is lower, but there are distinctive patterns in the product purchases. The purchase patterns exist by product choice combinations, customer type, vertical, usage, geographic region, price point and so on.
“I know that there are patterns in what our customers buy,” added Martin. He was previously the VP of Sales and has knowledge of what products customers buy. “We need our sales reps to have access to that intelligence so that they can be better advisors to customers and close the deal faster. I have sat in sales meetings with a company exactly like the one we sold to 3 months ago, and watched my sales rep grill the prospect on product requirements. That hurts our sales more than anything else.”
In a recent blog, Michael Gerard from IDC wrote a very interesting article on the same topic. He mentions a story where a CIO from a $10B+ company had to continuously teach a vendor sales reps what he had purchased from them in the past. The article goes on to state that this can lead to poor credibility on the sales front lines.
There is a solution for this problem. Every company is sitting on tons of sales data. It is a wealth of data that can reveal what their customers are buying and where they are willing to spend their money. Emcien offers analytics that auto-detects the choice combinations in sales data. What are they buying? What are popular product choice combinations? What are popular choice combinations by vertical? What combinations are profitable? Which ones are not? This is the demand intelligence that makes each day NOT be ground hog’s day!!
As a first step, it is invaluable to arm your sales reps with this intelligence so that they can be smart on every deal. In fact, every sales channel can benefit from this intelligence. Here are a few examples -
- Sales reps can use demand intelligence to be better advisors to customers, convert requests to quotes faster and recommend good choices to close the deal. Even a simple report on what is the fastest selling product choices will empower your sales reps, and drive to the bottom line.
- Your ecommerce site can use demand intelligence to quickly recommend the best products to help the customers to self-serve on and make better decisions.
- Inside sales team can use demand intelligence to quickly complete quotes and close the deal. I was talking to an inside sales rep and he told me that his biggest challenge is quickly responding to a quote with a price. ”We have done this so many times before,” he said. “Why does it take us to long to get a price? We lose deals because we cannot respond fast. The first to respond with a quote locks in the deal 90% of the times, even if the price is higher. We lose deals by being slow.”
- Call centers can use demand intelligence to cross sell/up sell based on buying patterns. The turn over in call centers is high. Automating the cross/up sell with demand intelligence will dramatically improve productivity and profit.
Quoting Michael Gerard “This is only the tip of the iceberg of course.” Demand intelligence can dramatically improve your sales performance, customer satisfaction and profit margins.
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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