This week LightHaus, a provider of Visual Customer Intelligence (VCI) solutions for retail enterprises and chain stores, announced that Foot Locker became their latest customer to deploy LightHaus VCI solution. Foot Locker  will be using the solution to measure customer traffic and sales conversion rates at all of its Champs Sports stores in the U.S. and Canada, as well as its Foot Locker Canada stores.

I reached out to Dr. Mario Palumbo, CTO of LightHaus to get answers to some of my questions on how LightHaus VCI solution fills the unmet industry need with its in-store video analytics solutions for retailers.

What is LightHaus Visual Customer Intelligence (VCI) solution and how does it impact Retailers? Can you provide few scenarios where VCI solution is a great fit?

Mario Palumbo: The LightHaus Visual Customer Intelligence system analyzes video from in-store cameras to measure customer traffic and engagement in retail brick-and-mortar stores. Retailers, like Foot Locker, user data delivered by the LightHaus VCI system to gain new insights into their store performance. For instance, by combing transaction data with customer traffic data into the store and browsing data for specific categories, displays or products, retailers can understand their conversation rate – the percentage of customers that purchase- on an hour by hour, day to day, and week over week basis. By comparing conversion rates over time, and across stores, retailers can identify high performing stores to learn from, and lower performing "opportunity" stores. With in-store shopper insights, retailers can drill down to find out why customers aren't purchasing.

LightHaus VCI is a good fit for any retailer who wants to increase their sales and wants to give their managers better tools to discover the factors driving purchases, and where problems and opportunities exist. LightHaus VCI retailers them understand what their customers are doing in their stores, and learn how to influence their customer's experience to improve sales. For example, in a retailer where service is essential to their customer's experience and ability to purchase, retailers are alerted to the need for more associates by reports comparing conversion rates to their customer to store associate ratio. A strong correlation between low conversion and high customer-to-associate ratio indicates staff are being overwhelmed and customer service and sales are suffering.

Another great fit is with retailers who want to know the effectiveness of their marketing campaigns. Because LightHaus goes beyond traditional traffic counting, which simply counts people entering the store, and goes inside the store to measure where customers go, where they stop and engage, and what they browse, retailers can measure conversion not only by store, but by category and product. With LightHaus VCI, marketers can see the impact of their campaigns not only on store traffic, but also on the number of customers that pass by the promotion and the number that stop to browse the promotion.

Using the VCI solution, can a Retailer predict the footfall and resulting sales for a future timeframe, maybe predict month over month sales increase/decrease and thus take corrective actions.

Dr. Mario Palumbo: With LightHaus VCI retailers can incorporate actual traffic data into their business processes – from workforce scheduling to sales forecasting. Instead of basing planning simply on historical transaction data, they can also use data the show the sales potential.

Also, using VCI on an ongoing basis retailers can establish traffic trends within their stores based on time-of-day, day-of-week, seasonality, and in response to promotional activities. This allows them to optimize their store layouts, merchandising, and staff scheduling to ensure they capitalize on the true sales opportunity

For a small store chain (let’s say less than 20-30 stores nationwide), how does the deployment work? What is the ROI a retailer can expect after deploying VCI?

Dr. Mario Palumbo: There are a couple of aspects to deploying the LightHaus solution. We've found it's important to think about both how the resulting insights will be used, and how the system is deployed.

The first step is establishing the business case. As LightHaus VCI is all about helping retailers increase conversion, we work with the retailer to understand their current conversion rates, the factors driving conversion, and the ways the retailer can influence them. This is where the retailer establishes a target ROI. While we can't share specifics, we can share that retailers are finding the VCI data reveals many "opportunities" and that even a small improvement in conversion rapidly pays for the system, and then adds significant dollars to their bottom line. Just some of the opportunities include matching labor to demand, increasing effectiveness of store layouts, merchandising and marketing, and increasing store staff focus on closing the sale.

Now, let's consider the in-store deployment. One of the things that's attractive to retailers, both large and small, is that LightHaus VCI is designed to be easy to deploy. The retailers deploy an IP camera for each area they want to 'SpotLight'- that is capture and analyze video from. In a typical mall format store this would include an Entrance SpotLight, and 2 to 3 In-Store SpotLights in key locations such as end-caps, promotion areas, or service areas. LightHaus works with leading system integrators who are familiar with the retail environment to install the required cameras and the LightHaus VCI appliance - a small form factor network appliance. Then LightHaus service reps remotely configure the system to ensure it is accurately measuring shopper behavior. The LightHaus VCI appliance processes video from each camera, or SpotLight, and sends the resulting data to the LightHaus VCI hosted server. No video ever leaves the store - only low bandwidth numerical customer intelligence data is sent to the hosted server, where a web-based data mining application allows the retailer to view and mine the data in near real-time from any standard web browser. Installing a typical store takes less than an hour.

How easy is it to integrate VCI with other LOB applications that a Retailer may be using?

Dr. Mario Palumbo: LightHaus is designed to allow retailers to easily access the customer intelligence data from other retail systems such as business intelligence systems and WFM through a simple and comprehensive Web Services API. Also to meet customer requests, we support standard CSV file import.

Over the last few months, there has been a lot of coverage on how Retailers are using Big Data. Wal-mart with its recent acquisition of Kosmix is one Retailer which  is in the forefront of this wave. Here is a collection of articles which discusses how Wal-mart is using Big Data.

How Walmart plans to use Big Data

Kosmix stands out for its ability to search and analyze connections in real-time data streams to deliver highly personalized insights to users. The platform powers TweetBeat, a real-time social media filter for live events. By using this intelligence, Kosmix is building a giant knowledge base called the‘Social Genome.’ This giant knowledge base captures information and relationships about entities such as people, events, topics, products, locations and organizations.

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By analyzing their social media activity, Social Genome can make recommendations about products, events or any other activity that the user is interested in. For example, by using publically available social media data, the Walmart product store can suggest product recommendations, based on recent tweets or Facebook wall posts.

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While the idea sounds great, doing this in reality is a huge problem — especially since there are thousands of data pieces flowing in a torrent from live data sources such as tweets, Facebook posts and blogs. The data flow was so fast that Kosmix could not rely on the traditional Map-Reduce or Hadoop framework that is typically used to solve Big Data problems.

“Social Media data is the fastest growing source of Big Data today. In addition to being Big Data, social media data such as Twitter also has a real-time nature — it’s not just Big Data, but also Fast Data. With mobile devices, location data is now a new source of both Big and Fast data,” explains Rajaraman, on the technical challenges faced by his firm while building the platform.

To address this Big Data and Fast Data problem, Kosmix developed its own in-house solution called Muppet, which processes streaming fast data in a lightening fashion, over large clusters of machines. Today, Muppet can manage and track data streams with billions of updates a day.

Getting a Handle on Big Data with Hadoop

Wal-Mart Stores, struggling to translate its brick-and-mortar success to the Web, is using free software named after a stuffed elephant to help it gain an edge on Amazon.com in the $165.4 billion U.S. e-commerce market.

As customers flock to social media, Wal-Mart expects sites such as Facebook and Twitter to play a bigger role in online shopping. By analyzing what social network users say about products on those sites, the world’s largest retailer aims to glean insights into what consumers want.

With its online sales less than a fifth of Amazon’s last year, Wal-Mart executives have turned to software called Hadoop that helps businesses quickly and cheaply sift through terabytes or even petabytes of Twitter posts, Facebook updates, and other so-called unstructured data. Hadoop, which is customizable and available free online, was created to analyze raw information better than traditional databases like those from Oracle.

“When the amount of data in the world increases at an exponential rate, analyzing that data and producing intelligence from it becomes very important,” says Anand Rajaraman, senior vice-president of global e-commerce at Wal-Mart and head of @WalmartLabs, the retailer’s division charged with improving its use of the Web.

Big data and the disruption curve

Big data projects are aimed at revenue growth, many efforts are being funded by business units and not the IT department and money is increasingly being diverted from large enterprise vendors.

Turning the Kaleidoscope of Data to Discover Value

Editor’s Note: This is a guest post by Manthan Systems. Read more about Manthan Systems at the end of this article or visit www.manthansystems.com.

Access to point-of-sale data has transformed the retail businesses by suddenly throwing open new insights from data that were hitherto not captured. This knowledge has empowered the retailers with an ability to understand their business better and use these insights for accurate decision-making.

Various statistical techniques can be used in the right combination to analyze data and arrive at trends and patterns that lead to increased sales and thereby directly impact the bottom-line and result in profitability. Using these techniques, retailers can categorize customers by the products and services they choose, identify patterns to plan cross-selling campaigns, analyze and target customers based on product-centric purchase histories and patterns, plan multi-product promotions based on customer response, arrive at customer probability to buy additional products, measure shifts in customer… behavior and locations, compare segments – the possibilities are endless.

Realize the connection

Retailers are aware of the fact that shoppers who buy one product frequently, (for example, hamburger patties) are more likely to buy a couple of other related products (for example, hamburger buns and fries). This probability is due to the amount of affinity that exists between the products.

While some product affinities, such as the one above, are obvious to observers and can be arrived at by using common sense, others can be less apparent without the appropriate data mining techniques; for example, Wal-Mart customers who purchase Barbie dolls have a 60% likelihood of also purchasing one of three types of candy bars (Forbes, September 8, 1997). This paired purchasing pattern can be attributed to the obscure correlation between the two products.

Every shopper’s basket has a story to tell

The items a shopper purchases during one shopping trip makes up the ‘market basket’. The various items in a market basket are correlated to each other with varying frequencies, presenting a picture of what may have driven the shopping trip: running short of a couple of essential ingredients for cooking, assembling all necessary items for an exotic meal or a birthday party, preparing to have guests over, stocking up groceries for the month

The selection also reveals the shopper’s profile to a certain accuracy and provides a glimpse of socio-economic attributes of the shopper: is the shopper a family person? Is there a child in the shopper’s family? Are there elders in the family? What age-bracket does the shopper fall into? Is the shopper an impulsive buyer? What economic section does the shopper belong to? The overall level or value of the selection is another factor that can be of significance for retailers. Such information when viewed on the time parameter reveals stronger correlations: among products, between shopper profiles and their product preferences, between the kind of shopping trips and the product preferences, between the time of the year and product preferences of a particular shopper profile, to name a few.

How business analytics can help

Applying these seemingly simple statistical principles of great practical use to the humungous amount of data in any large format retail is a Herculean task. Sifting through this data and reaching any valuable conclusion is quite like looking for a needle in a haystack.

Business intelligence (BI) systems are often looked at as a solution to this challenge. However, the piece that separates a generic BI system from the one that facilitates retail decision making is advanced analytics. BI systems designed specifically for the retail industry and powered by advanced analytics can help drive the functional theories of mathematics in retail decision making. The analytics angle brings in a scientific base for choices that were made randomly or based on observation and experience earlier, guaranteeing a considerably higher success rate. The prescriptive and guided nature of retail-centric BI systems, along with their out-of-the-box functionalities, makes it possible for unskilled personnel in the retail chain as well to use these statistical techniques in their area of operations to arrive at benefit-driven decisions. An attempt to assemble a BI tool-kit for such analytical requirements can prove hazardous leading to uncertainties and perils involved in the never-ending process of adding blocks to map newer business demands and analytical needs.

Retail BI applies the principles of product affinity and market basket analysis to point-of-sale data to reveal concealed relationships between products and discover customer behaviour patterns. Such analysis provides insights into the types of products customers usually buy together, the time of year when the sales for a combination of products increase, destination items that attract customers to the store, and reasons for a sudden boost in the sales of a specific product. The analytical abilities of retail BI can identify correlations between customer profiles and product purchases and store visits.

With the help of retail BI, product affinity and market basket analysis can be used by the marketing and merchandizing teams at various levels in a large format retail scenario. Market baskets can be profiled and classified into categories such as grocery basket, special occasion basket, weekly shopping basket based on the objective of the shopping trip as revealed by the analysis. This information can be used for planning daily promotions to drive more trips to the store by offering a discount voucher for a week or to increase the basket size during each trip by reducing prices of certain products or offering special discounts/gifts. Similarly, product affinity data can lead the store manager to identify the products that most shoppers look for in the store and place them in an easily accessible area or identify the products that are bought together most often and display them in close vicinity or arrange the products identified as impulse purchases in an attractive manner to make the impulse irresistible.

Decisions related to the selection of products on discount, special offers to a particular segment of customers, gift vouchers on certain products must be based on scientific data in order to derive maximum value from such initiatives. The trends and patterns that are revealed by retail BI can be used for:

• Improving the effectiveness of marketing, sales and merchandising strategies
• Planning strategic initiatives such as periodic promotions, campaigns, special offers, price changes, cross-selling, product-pairing
• Correlating store performance with overall market performance
• Planning the store layout for more effective product placement and shelf presentation with appropriate prominence for impulse purchases, seasonal purchases, destination/anchor products

Retail BI can turn the kaleidoscope of data in infinite angles leading to endless possibilities that help analyze and use the data to arrive at trends and combinations in retail decision making.

Manthan Systems produces cutting edge analytic solutions for global retail and CPG organizations. Manthan's breakthrough solutions, under the brand name ARC, transform the way retailers use analytics driven decision making for strategic advantage. The ARC product portfolio spans the entire spectrum of retail decision making with role-based, pre-built applications, and includes products for merchandising analytics, financial analytics, customer analytics, supplier collaboration analytics and enterprise retail BI. These award winning products provide a significant edge to an organization’s analytical capability and maturity, and are proven to deliver unmatched business benefits in a remarkably short timeframe. Manthan’s experience spans a wide range of retail segments and formats, having transformed decision making for some of the biggest names in retail across the globe. For more information, visit www.manthansystems.com .

Retailers need to leverage every successful opportunity in the coming year, but do they have the visibility to identify them in a timely way?

Distributing timely and accurate information across the retail enterprise to support distinctive business successes is the envy of every retailer.  Without the right systems to distribute the data and tools to bring focus to the information, opportunities quickly fade from view.  Does your business get the word out and empower field managers, analysts and executives to initiate change?

IBM (NYSE: IBM) today announced that NYC-based apparel manufacturer Bernard Chaus is using SKYPAD delivered analytics through cloud computing to improve sales, analyze in-season buying trends, and track the hottest selling products by store -- down to style, size and color. SKYPAD is enabling Managers at all levels of the organization to make informed decisions based on the timely and accurate receipt of sales data, providing the flexibility to react globally and locally to instant trends. 

Bernard Chaus, Inc., a midsize manufacturer and distributor of women's career and casual sportswear, was looking to streamline its sales and merchandising efforts and create a detailed, accurate view of weekly sales trends for their decision makers. With previous manual data collection processes and Excel spreadsheets, there was no guarantee that sales information was correct or all inclusive, compromising the company's ability to react to fluctuations in sales.

Working with IBM and IBM Business Partner SKY I.T. Group, the company developed an analytics-based solution called SkyPAD that provides a weekly report of key business metrics via a web-based dashboard.  The dashboard enables improved decision making across product pricing, assortment design, production and distribution.  Using the system, Chaus can quickly spot fast selling items, shift products between stores, adjust pricing and make in-season adjustments based on proven consumer demand.  Chaus also has the capability to instantly compare profit margins across different retail outlets to identify future brand positioning strategies. 

SKYPAD Enables Retailers to:

- Get comprehensive retailer reports faster

- Automate reporting into ONE easy to use platform

- Easily analyze data based on YOUR specific initiatives

- Seamlessly integrate spreadsheets, EDI and portal data into ONE view

- Increase accuracy, productivity and team focus

- Utilize images, scalability and consistency not possible with Excel

Read More

Retailers are adopting new strategies in targeting its loyal customers (WSJ, Registration reqd) by using Analytics and advertising promotions on an individual basis. This is a shift from earlier times when Retailers would send similar email promotion to all. Retailers like Sears, Gap, Target are using analytics to tap their most profitable customers and shying away from TV commercials

It's an adage of the business: Persuading a satisfied customer to return is cheaper than attracting a new one. Now, in the struggle to do more with less, that concept is becoming even more important.

Acquiring a new customer costs about five to seven times as much as maintaining a profitable relationship with an existing customer, says Marc Fleishhacker, managing director at WPP's Ogilvy Consulting, which designed the campaign for Sears.

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Sears and Ogilvy have developed a system to identify the categories of merchandise Sears customers have purchased in the past and to measure the chance that they will buy those sorts of items again this season. That helps Sears determine the type of emails and point-of-sale offers to aim at individual customers.

When customers buy an item online, Sears confirms the purchase with an email including a promotion tied to that product. A person who buys a new appliance at Sears.com might get an email offering a deal on the store's extended-warranty program.

More and more, we will see Retailers using such Appliances to perform complex analytics such as tracking stock wastage and performing market basket analysis. 

Netezza Bags Tesco

Netezza (NYSE Arca: NZ) today announced that Tesco has installed its market-leading data warehouse appliance to power market basket analysis and track stock wastage. The move from Teradata for this project was designed to improve the performance and class of technology in Tesco’s data centre, while driving down all associated costs.

Widely recognised as the world leader in understanding retail customer behaviour and loyalty programmes, Tesco has tracked different data warehousing vendors coming to maturity in the market, and is keen to keep pace with business change. Marcel Borlin, Programme Manager at Tesco explains, “We examined a number of vendors in great detail. We were looking to realise savings; not only on the purchase cost, but on the total cost of ownership too. The physical footprint and power and cooling requirements in the data centre were also important factors. The Netezza system needs literally a fraction of the space, power and cooling of our existing systems. We also needed better performance than our current Teradata platform can offer, and our Netezza system is performing very well on both queries and data loads; up to five times faster.”

Great article on ways to track offline conversions and the need for 360°multi-channel analytics. A must read for everyone who is involved in Multi-Channel retail or getting into the same.

Tracking Offline Conversions: Hope, Seven Best Practices, Bonus Tips

Don't forget to read the comments too, a whole lot of discussion and clarifications are happening on the article's comments.

Retail Store Ops blog has a post on how retailers are adopting sophisticated traffic counting systems and developing conversion rate analytics to track how many shoppers are actually making purchases.

Given the economic slowdown and increased pressures to show top and bottom line growth, missing out on capturing customers while they are in a store is becoming an opportunity retailers can no longer afford to miss. Major retailers such as Virgin Megastores, Marks & Spencer, and Crabtree & Evelyn have served as a few of the early case studies for the benefits of measuring and improving conversion rates.

There are some interesting numbers thrown in the blog post. For example,

Virgin has credited the analysis with uncovering variations of up to 20% in average transaction values between stores, as well as a 15 point difference in conversion rates between its highest and lowest performing stores.

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Once retailers start collecting the performance analysis from individual stores, they are often surprised by the results. “If you were to ask a retailer how many shoppers they convert, the assumption is typically north of 50%,” said David Smyth, Vice President of sales operations for Experian FootFall. “In reality, the average conversion rate ranges between 20% and 40% for most retailers. Using that average, that means about 70% of shoppers are leaving the store without buying anything. That means retailers are leaving an awful lot of money on the table.”

So you can understand the importance of Conversion Rate Analytics. Even a 1% improvement could potentially mean millions of dollars. But conversion rate analysis is more difficult then what it is for Online stores. Many retailers measure the sales and try to deduct the store performance on the same. Factors like store traffic are ignored in these cases.