Tag: merchandising
Connect With Us
Every Item In Your Store Is Linked To Other Items
Retailing in the 21st century is no different from a high-performance, high-adrenaline sport. And like athletes, retailers are forever seeking that cutting edge through state-of-the art technology and analytics. Market basket analysis, also sometimes known as affinity analysis, has emerged as the next frontier in retail merchandising and promotions. Market Basket Analysis (MBA) enables retailers to analyze inventories in their customers’ “baskets” and identify their buying patterns.
MBA has disrupted the retail industry in a major way. According to a study carried out by the FactPoint Group, a Silicon Valley research and consulting firm, more than 50 retailers with revenues from $400 million to $24 billion, were “familiar” with MBA and were “looking to extend their capabilities” in that area. What MBA brings to the table includes quite an impressive array of qualities that most “non-MBA enabled” companies would be challenged to achieve – increase in order size and order value, improved customer service and product availability, increased sales, more customers, better customer retention, smart enterprise, and much more.
Every item in your store is linked to other items. "It is quite an amazing web of connectivity"
Understanding Your Product Affinities is Core to your business
Every retailer wants to leverage the true value of brand identity and promotions. It is hardly surprising that without proper MBA, you run the risk of eliminating one product from “Jack and Jill” items. With hundreds and thousands of items, without the right tools it is challenging to quickly see the product affinities. There are numerous examples of supermarkets pruning products only to receive a flood of calls from customers. How can I make greek salad without feta??
At the category level, the affinities can unravel how price fluctuations on one item in a category can change the dynamics of the entire shopping basket. For example cigarettes prices are constantly fluctuating, usually on the rise. In convenience store chains the fluctuations will impact all categories with strong affinities with the cigarette category. A customer walking in with a $20, will have less to spend on other items if he buys tobacco products. So – what items are those typically? What items will see a sales drop as tobacco price increases?
Understanding the product affinities is core to assortment planning, merchandising, product availability and customer service. Every item in the store is linked to other items. “It is quite an amazing web of connectivity”, quoting Roy Marsten. Emcien’s analytics unlocks that product affinity web, and presents the entire list of affinities so it’s easy to understand and drive business decisions.
This intelligence is key to driving successful retail decisions.
Your Products Are Connected!: Understanding how your products are connected in the ‘eyes of your customer’ is key to increasing sales. Every product has an average basket size and a list of items is is typically bought with. This of course can change with seasonality, promotions, price fluctuations, etc. Ranking your products on these metrics will present the best items for promotions to increase total sales.
Cross selling and up selling: Merchants go to great lengths in assembling cross-product promotions with signage, proper training, etc. If you are a home improvement retailer, you’d want to sell your services along with appropriate furniture and installation. Studying the affinities reveals whether your primary merchandising is consistent with the related cross selling. Measure the sales rate of the primary product as well as the “supporting” products.
Value your customers: Customer is always the king (or queen!). As a retailer in women’s clothing, you’d want to know what percentage of your customer’s transactions included a particular item. After this, you need to identify the value proposition of the merchandise. You can also expand your analytics across geographies and time periods, and measure the buying patterns of your customers.
Repeat business (Loyalty): Calculate the frequency with which your customers arrive at your establishment(s). Connecting the loyalty programs to affinity analysis can dramatically increase the success of your loyalty programs. From the frequency, you can identify their purchasing trends, “eyeballing” trends, consistency in purchasing a particular brand, store visit patterns, and the right space to place and promote your merchandise.
Brand and lifestyle: You can calculate brand loyalty based on shopping behavior. Whether your customers are buying high-end products or in-house products, the insights you get will help you align your brand strategy to your customers.
To sum it all, understanding what Market Basket Analysis can do for you is not rocket science, and you need not be an Ivy League graduate to comprehend its dynamics. It all boils down to listening to your most prized assets – your customers, their transactions, and formulating a comprehensive roadmap to incorporate the right analytics tools.
Assortment Planning and Up-selling based on “This Sells With That”
Walmart’s (formerly Wal-Mart) announcement of a SKU rationalization project contained in this year’s 10-K filing with the Securities and Exchange Commission confirms the importance of this initiative for all retailers. In SKU (Stock Keeping Unit) rationalization, a retailer examines the profitability of items and vendors as a whole. When done in a linear fashion it results in lost sales and bringing back the SKUs.
SKU rationalization projects look for “What items are bought together” so that retailers and distributors can improve assortment planning. As shoppers, we all know that we buy items in groups. It is the job of the retailer to figure out what kind of stuff we buy together, so that they can optimize their assortment planning. Simple example – If I cannot buy both bagels and cream-cheese at the same time, I will go to a store where I can find it!
SKU Classification Based on Frequency of buys and Product Relationships
SKU analysis for assortment planning is based on two key metrics:
- The frequency of buys. This is a metric that measures true popularity of an item based on how often customers buy this product. For measuring popularity, it is better metric than volume as it is not skewed by one-time large volume purchases by a few customers.
- How often this item is bought with other items. This metric is a measure of how strongly correlated this item is with other items that you sell. If an item is always purchased with another item (like bagels and cream-cheese), it is very important to know the “often bought with” items, and ensure that they are stocked together and in the right proportions. Not having one item from a basket of high affinity products will result in loss of the customer.
These two metrics also apply for Amazon-esque suggestive selling for online sales. Items that have high correlation with other items are candidates for suggestive selling, up-selling, cross-selling and add-ons. For example, this would be a way to detect that cables, cartridges and paper that are bought with a particular printer. So when that printer is bought, you can automatically suggest the other items as add-ons. (Not to get too technical here, but the suggestions are not symmetrical. So – you cannot suggest a printer when a customer buys paper!)
The implications of these product relationships cannot be emphasized enough on your merchandising strategy and your supply chain planning. Manufacturers, distributors and retailers struggle to manage thousands of SKUs. This SKU classification presents a methodical approach for assortment planning to maintain the most profitable portfolio.
SKU Categorization For Merchandising, Up selling and Cross selling
The second chart presents a more detailed discussion of the SKUs based on frequency of buys and affinity with other products. (Affinity simply means “this items sells with that”. )
I - Items that have low-frequency/ high correlation are important to detect. These are trouble-maker SKUs. As companies goes though SKU rationalization projects, these items often end up on the chopping block, only to brought back again because they caused lost sales. These items are difficult to identify and there is a need for sophisticated analytics to easily identify these items.
II – Items that are bought in high quantities, but always with other items are great candidates for merchandising and bundling. They are a natural for creating sales lift and revenue lift. It is often counter-intuitive, but your #1 top seller may not be in the #1 pair of top selling items. That is why linear analysis of the SKUs based on volume or frequency results in incorrect merchandising.
III – The low frequency/ low correlation items are the targets for SKU rationalization projects. However, these items are very difficult to identify. Hence SKU projects typically end up cutting the wrong SKUs. We call these items Low-Loners. If you are a distributor, you do not want to carry these items. They are perfect candidates for drop-ship.
IV – Items that sell in high frequency, but usually on their own, require high service levels. We call these Hi-Loners. Examples of these items are cigarettes and gas at a convenience store. And by the way, beer also falls in this category. And please do not believe the beer and diapers myth! It is a myth!
The challenge with SKU management is that companies make decisions based on product relationships from hear-say, industry veterans or tribal knowledge. I think that’s how the beer-diapers myth was started! Across thousands of SKUS, and with fast changing demand patterns, this results in errors, and not a sustainable process for assortment planning and SKU management. There is too much at stake to base a companies sales and revenue on hear-say.
As SKU management is getting a lot of attention, there is need for robust solutions based on real customer buying behavior, to help companies maintain their SKUs on an continuous basis. The value is high sales, higher margins and improved customer service.
Comments Off
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.
Comments Off
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.
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!
Comments Off
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!
Comments Off
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!
Comments Off
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!
Comments Off
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!