Tag: buying patterns
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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 »
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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We don’t need to get new customers!
We don’t need to get new customers!
Very bold statement from the Macy’s CMO Peter Sachse in his keynote speech at the Retail Innovation & Marketing Conference talking about a shift in company focus.
Here’s how it all started: Last year, Macy’s embarked on an intense research project to better understand their current customers. They conducted dozens of focus groups. Talked with nearly a thousand people walking out of their stores. Leveraged data from NPD Group for a holistic understanding of their customers. Combed through all of their transactional data to find themes in buying patterns and shopping habits.
The overwhelming finding? For Macy’s, “What we don’t need to do is get new customers,” Sachse said. Instead, “we realized that all we need to do is take care of those who already love us.”
The company has set out on a goal to encourage each existing customers to visit the store one more time each year. “Half the battle is won if we can get them to walk into our store,” Sachse said. “And if we convert them during that visit, our comp store sales will explode.” To accomplish that goal, he said, “We had to get a lot closer to the customer,” which has led to the company’s new strategy of customer-centricity.
I could not agree more! Macy’s needs to understand the buying patterns of its current customers and serve them better. This will result in higher customer satisfaction, higher repeat sales and higher profits. If you do not know the buying patterns of your current customers, getting more customers is NOT going to help. Mr Sachse is absolutely right!
Companies today spend tons of money trying to get more customers. Very few companies have a finger on the pulse of the buying patterns and trends of their current customers. What is the point of getting more customers if you cannot serve the one that you already have? Is it just busy work? Or is it because with thousands of SKUs, companies do not know how to keep up with customer buying patterns?
Congratulations Mr Sachse! I look forward to walking into your store and finding the right stuff.
BI is for IT, But Analytics is for the Business User!
I’ve just come out of a meeting with a business user who is passing up on a well-known BI software package. “It takes too many IT resources to implement. My IT guys love it but I cannot afford the cost/time,” he said.
As companies drown in data, BI is a very expensive route to try and gain value from the data. Mark McDonald, head of research for Gartner Executive Programs, has a very nice article titled Without the Business in Business Intelligence, BI is Dead!. Sounds like – “The King is Dead. Long Live the King.”
BI has been built for the IT community. It is an old-school solution built on the heavy weight model of technology. That model rests on the acquisition, installation and operation of technology based on a significant upfront investment that is earned out over a period of time. This has been the investment/implementation path for enterprise ERP/SCM/CRM/PDM software packages. BI is in this class of software solutions, and results in an expensive and less responsive solution. Mark McDonald calls this the “old ‘heavy weight’ model of technology”.
The top two categories for Gartner’s predictions for 2010 are Cloud Computing and Analytics – these are both directions that are a far cry from the old world of heavy/expensive/pay upfront software. BI in its current form is completely out of step with these predictions and where the market is heading. So, why did Gartner renamed BI to Analytics? In doing so is BI going to magically transform from its old heavy weight form to a new lean enterprise 2.0 form? Long Live the King?
Emcien offers pattern-based analytics that easily takes sales data in any form to reveal customer buying patterns and trends. The technology has completely eliminated the need for data models, structures, mapping, etc. Emcien’s pattern based analytics technology was created explicitly to overcome the ‘old heavy weight model of technology’. Pour your sales data in, and watch the customer buying patterns. “Like Google analytics for sales data”, our customers told us.
Mark McDonald has prophecy for BI that I think is dead-on (pun!?). “On a radical note, we are seeing some early signs that companies are looking to use social media/web 2.0 technologies to address business issues that were previously assigned to BI.
Lighter weight technologies handle tacit information and semi-structured process support better than BI solutions that rely on structured and standardized information.
Our customers completely agree with Mark McDonald. Quoting a VP from a Fortune 500 company, “We have lost the appetite for million dollar software and long implementations that consume IT resources”.
However, the need for harvesting intelligence from data is not going away. On the contrary, it has never been more important than it is now. The data hides jewels of intelligence that companies need to act on NOW. But that is only possible if Business Intelligence is not a technology but a capability for the enterprise. Long Live the King!
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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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Increase Sales With Analytics on Sales Receipts
Increase Sales with Purposeful Analytics On Sales Receipts
The sales receipt is a neatly itemized list of purchases. Every purchase comes with a specific need, and hence the sales receipt is the true voice of the customer. As demand patterns change, the sales receipt data can reveal tremendous intelligence on what customers are buying, the changing trends and what the future purchases will be. “Stores Face New Kind of Shopper” is a very interesting article by Ann Zimmerman and Rachel Dodes in The Wall Street Journal (Monday, December 28th 2009).
The financial crisis has dramatically impacted sales in all markets. Over the last two years sales have plummeted, consumers have disappeared and profits have evaporated. The financial crisis has caught us in a time of tremendous over capacity. In the B2B markets, companies have been dramatically shrinking capacity to match the new level of demand. In B2C markets, retail experts generally believe that the US now has more stores than consumer demand can support.
Customer buying patterns are dramatically changing as capacity adjusts to the new level of demand. The financial downturn further impacts this change, as customers look for new ways to stretch their money. To complicate things further, customers today have many choices of products, channels and price point. The internet has become a primary source for browsing and comparison shopping. This extends the reach of the customers, and puts pressure on companies to cater to wider product choice selection. As these shifts continue to change buying behavior, companies must have the capabilities to stay ahead of the changes. With the speed of change in products, companies need to adapt fast and stay in tune with changing demand.
The good news is that the sales receipts reveal these changing trends and buying patterns. However, this requires purposeful analytics designed to convert sales data into actionable tasks. I would also like to mention that sales data has a unique structure and characteristics. The purpose of the analytics is to reverse engineer the sales data to determine what is selling. If your product has a lot of feature choices, you can get insight into the popular choice combinations. If you sell lots of individual items (i.e. large number of SKU’s), you can get insight into what are items that are commonly grouped together. Emcien offers analytics designed for sales data. Emcien’s advanced analytics cal also give you intelligence into what choices cause the selection of other choices. Armed with this insight, you can manage your product offering to always stay ahead of the trends.
With purposeful analytics designed for sales data, you can get insight into -
- What product choice combinations are popular?
- How do the choice combinations vary by channel?
- What choice combinations are profitable?
- What are the changing trends and what choices will sell in the future?
As the market shift continues, this level of demand intelligence is mandatory to stay profitable!
Do Customer Buying Patterns Exist?
Customers have to make choices in order to buy configurable products. Do they make these choices at random, or are there patterns? When we look at the sales history for a configurable product, like a car or a computer, can we tell if customers have just been flipping coins and rolling dice? Or do their choices hang together and make sense? To answer this question, we would have to look at how they buy combinations of options. In the previous post, I took a pizza as a simple configurable product, and looked at how customers ordered pairs of toppings. Just by looking at the sales numbers we could detect that the selection of pineapple and Canadian bacon are not independent. Even if we had never heard of a Hawaiian Pizza, we could discover it in the data.
Even more information is hidden in combinations of three toppings at a time, or four toppings at a time. Any combination of toppings will have appeared on some of the pizzas that have been sold (or maybe none). The relative popularities of all the different combinations has a clear message: customers are not flipping coins. Some toppings naturally go together, and others do not. Pepperoni, broccoli, and anchovies is just unlikely. If a particular pizza restaurant has a few “house specials”, like the Meat Lovers and the Veggie Delight, we can see them in the data, even if we don’t know their names.
What is true of the pizza is also true of other configurable products: computers, trucks, tractors, lighting fixtures, industrial fans, and so on. All products that have variety. Customers make choices, but not by rolling dice. There are combinations that go together and combinations that do not. A pizza maker can juggle the preferences of his customers in his head. But when a product has 30 or more features, intuition is overwhelmed. The number of combinations explodes so fast that the unaided human mind can’t see the patterns. At this point, mathematical models and intense number crunching can reveal the patterns and let the product manager for a line of trucks be as confident as a pizza maker.
Do Customer Buying Patterns Exist?
Buying patterns are real, and they manifest themselves in how customers buy combinations of options. With the computing power we have available today we can detect and capture them. These patterns can then be used to design “house specials”, forecast future sales, and guide customers to what we want to sell them.
So, who else is talking about customer buying patterns?
Intel Talks about Changing technology buying patterns
Customer Buying Patterns have Changed. What’s Your Plan?
An entire report that summarizes the results of a consumer usage and purchasing pattern survey conducted in March of 2007. The survey was conducted with In-Stat’s Technology Adoption Panel (TAP) — a dynamic, online panel of more than 19,000 technology users and decision makers. Over 1,400 technology users responded to this focused survey.
Findings in this report include consumers’ time spent on PCs, when they last purchased a personal-use PC, the PC’s features/form factor/usage, the desired features of future PC purchases, changes in usage patterns, and consumers’ thoughts about new technologies.
The changing patterns include -
- When consumers are likely to make their next PC purchase.
- The features consumers state they want
- The features consumers state they really want, based on changes in their usage/buying patterns.
- How consumers view new technologies
However – buying patterns are constantly changing. As social networking grows, we are watching new markets emerge every day. There is gold for companies who can continually detect these patterns and offer the right products and feature mix.
Is your sales history self-encrypting?
Emcien’s mission is to find the actionable intelligence that is hidden in the sales history of configurable products. We call this SKU intelligence. Many companies, however, save their sales history in a way that keeps any patterns hidden forever. I call this self-encryption. Many of these worst practices began as a way of saving space at a time when storage space was expensive.
A configurable product is one where the customer has to make choices to customize the product to his own particular needs or preferences. The valuable patterns are in the way these choices are made. The sales history should be at the right level of abstraction: in terms of the choices that the customer made. Here are four ways you may be encrypting your data.
- SKU Numbers. SKU numbers identify unique product configurations. They are a great shorthand for keeping track of what has been built and what is sitting in inventory. But if the sales history is kept in terms of SKU numbers, and the definitions of those SKU numbers are stored in a different place, then you may not be able to decipher your own history. By “different place” I mean a different database, different computer system, or anywhere that is not part of the history itself.
- Part Numbers. Customer orders get translated into Bills-of-Material (BOM) so that the requested item can be built and delivered. But what happens to the order afterwards? Often it is saved in terms of the part numbers. The customer ordered “2GB of RAM”, which became part 123-XYZ-645A. This was the right part number for 2GB of RAM from a certain supplier during a certain period of time. Remembering 123-XYZ-645A may be important for some warranty issues, but it is the wrong level of abstraction for understanding the customer. Many customers ordered “2GB of RAM”, but they got many different part numbers (different suppliers at different times). Part numbers change constantly, and unless a complete trail of part number changes and equivalences is maintained, a history in terms of part numbers is irretrievably fragmented.
- Standard Options. Most manufacturers make different models of their products, and the different models come with different “standard options”. The sales history doesn’t mention these options because there would be so much repetition (let’s save space!). The problem is that the set of standard options changes over time, even though the model names stay the same. Which options were standard on Model ABC in September 2007? Who remembers?
- Product Packages and Option Bundles. This is similar to the standard option problem. Some set of options is bundled together and sold as the “Sports Package” for some period of time. So the sales history says “Sports Package”. What was in the Sports Package in September 2007? Who remembers?
The sales history should be self-contained, with a record of each unit sold, expressed in terms of the options bought by the customer. If some options were implied by others, but could have been different, then they should be spelled out. If the data is saved in the right way, then the patterns in how customers buy the product can be revealed.
The difference can be dramatic. The message below appears to be gibberish.
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But suppose we use the key that Dan Brown uses in “The Lost Symbol”: the magic square discovered by Benjamin Franklin.
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Then we see the hidden message: “Emcien can easily find the hidden treasure in your sales history”.
The value is that customers are speaking to you when they buy your products. This is the true Voice of the Customer (VOC). But due to the data encryption issue, companies are blind to this intelligence. Unleash this intelligence, and you can drive higher sales and margin by serving the customer with the right choices.
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