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Top 3 Survival Tips for Manufacturing
1. Have visibility into what is selling
2. Streamline your product offering based on sales
3. Customize your supply chain to your product offering and demand signal (Read as “educate the supply chain of the product based on sales”)
A manufacturing operation that is disconnected from sales is bound to have high inventory and poor capital utilization – both being extremely detrimental to profitability. continue reading »
Increasing Order Size With Basket Analysis
I came in to buy milk and I am walking out with 10 things in my basket. The man behind me had only one item in his basket. “How do you do that?” I asked. “It depends on what you come in to buy,” he responded.
There are a few “seed items” in the store that drive additional sales because of key concept ‘this is often bought with that’. These items are often found together in customer baskets and orders. Smart retailers will put these items as far away as possible, so that you have to walk through more aisles to get from one item to the other, in hope that you will buy more along the way. Bread and milk is a good example of that. The reverse is also true. For items that are often bought together, if the store does not carry both, they will lose the customer.
Every retailer knows that it is very profitable when a customer comes in to buy one item, but ends up with many more in his basket. Understanding the product relationships in the market basket is key to driving up the order size or basket size.
Understanding the Customer basket make-up
A retailer typically carries thousands of items. A small convenience store may carry 1,500 items. A grocery store typically carries 15,000. And the super stores like Wal-Mart and Targets carry well over 25,000 SKUs in each store.
Insight Into Customer Baskets and Product Relationships Based on Buying Behavior
The SKU management is a tremendous challenge because the buying pattern is truly a long tail. Retailers know their top sellers; these are easy to identify, but the frequency of buying falls of very sharply. The chart shows an example of one retail store operation over a 3-month period. The store carries 25,000 SKUs, has 100,000 transactions per month. The analysis covers a 3-month period, and shows the distribution and popularity of SKUs based on the frequency of purchase.
Here are some quick stats for insight into the baskets and buying behavior – The most popular SKU has a frequency of 3,435. That means is has been bought in 3,435 baskets. The frequency of the 100th most popular item drops off to 225. That means it is only in 225 baskets over the 3-month period. There are 4,000 SKUs that are bought only once. But the really interesting fact is that 1,800 SKUs are bought together 98% of the times. None of these 1,800 SKUs are top sellers! But when they are purchased, they are very often paired with other items. This intelligence is key to increasing basket size and ensuring the store is carrying the right items. SKU rationalization analyses that view each SKU as an independent item, that is bought in isolation, will result in incorrect merchandising and lost sales.
There basket analysis also showed the low-frequency/high-correlation SKUs. Every retailer knows the challenge with these items. These items sell rarely, they sit on the shelf for along time, and when it is placed in a basket it will only sell if the paired item is available! These are problem SKUs because they are capital hogs and always show up in inventory issues.
Insight into the basket make-up and the product affinities based on buying behavior is key to merchandising and increasing order size. Merchandizing, up selling, cross selling and add-ons based on buying behavior results in increased sales and enhanced customer experience. On the other hand, suggestive selling based on tribal knowledge and ‘he said/she said anecdotes’ will result in poor results and loss of customer good will.
Adding one more item to 10% of the baskets can increase sales by 5%
Sales Impact Of Increasing order size
The basket size or order size analysis shows the revenue potential of increasing the order size. The chart shows a typical basket size analysis and the upside opportunity of increasing order size. The results from this case study showed that adding one more item to 10% of the baskets can increase sales by 5%.
Manufacturers, distributors and retailers offer thousands of products. There is a significant opportunity to increase sales across all channels with knowledge of product relationships (what items sell together), when and where. It is commonly agreed that B2B purchase behavior is “need based” while a large percentage of B2C sales is emotion based. Hence, in B2B commerce, the product relationships have to be highly accurate to be relevant.
Quick review of definitions:
Frequency – Number of orders that contain this item
Volume – Number of items sold.
The volume of an item may be high because one customer bought a lot. However, frequency is better measure of popularity and is not skewed by a one-time large volume sale. In fact, SKU analyses will often remove large volume buyers to reduce this bias.
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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Why the need for Intuitive analytics?
I just finished meeting with Kathy, a customer who is very analytical and has done an amazing job delivering value on data intelligence for her company. I spend an hour with her and she described the process of implementing tools to gain value from data. “This has been a painful process of exploration” she said. “We are a team of analytical types, and we love to play in the data. Not all companies can afford a team like this. In my last company we could not afford it. And companies cannot compete effectively if they cannot gain intelligence from their data!”
I asked her the challenges with the project and lessons learned. Kathy is a data genius, and there are very few folks like her. Here are some of the points that she made.
- “What am I supposed to do?” The analytics tool needs to answer this question for the business user. It also needs to be very easy to use and understand. This comment reminded me of another conversation with a BI user, and his pet peeves with his BI tools – “I do not know what question to ask!” He said. “Asking intelligent questions is most of the task!”
- Tools for Business Users. Not R&D. Not IT. Kathy went on to comment about the dire need for tools for the Business Users. Companies are running very lean. The last rounds of corporate downsizing have been brutal. Typically, the first teams to go are the analysts and R&D teams. Hence, if the tools are hard to use, now they will never get used. Business leaders are getting smart and they want tools that their line-of-business folks can use. This can help them get value in the line of business.
- Actionable Results. The old slice-and-dice data capability is not useful. Ask may questions and I will answer. The business units are too busy and do not have time to “play in the data”. The tools need to intuitive and give you actionable tasks.
- Like Google Analytics – The words “Analytics” is always associated with webs stats. That is the most commonly understood context of the word. However, there is far more data residing and building up in companies, than there is web traffic and search strings data. We need the same level of analytics, easy to use and intuitive tools for corporate data as we have for the web stats. Analytics means computing a relevant result and presenting easy to understand solutions. Like Google analytics for web traffic. Google analytics presents analytics on web stats; very domain specific, turn it on and off you go! Analytics for companies needs to be like that. Domain specific. Turn it on. Go Live. Get results. Get value!
- Need to think like B2C apps. This was an important point. Why do business users have ugly, clunky software that is expensive to implement and difficult to use? How can they be effective making those million dollar decisions? When the new wave of kids, who grew up with iPods and Facebook, enter the work force, will they throw out all the clunky enterprise apps? Absolutely!! Replace them with with easy to use, Facebook like apps. Business analytics needs to be that easy to use.
Great opportunities! We need to build applications with the user in mind. What will business applications learn from iPod, Facebook, Linkedin, ……
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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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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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