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December 23, 2009

Happy Holidays !

December 21, 2009

Analytics in Retail, CPG and Distribution

I keep on getting queries on use of Analytics in Retail Industry. Now I have tried to answer all those queries over here.

Problems and Issues in the Industry:Let us first see what the common worries exist in the Industry. I have tried to make a list of issues that is constantly taking the head of Business Managers.

  • Where to place the Production facility?
  • What is the most appropriate stocking point?
  • Which merchants should I buy from?
  • What is the most suitable sourcing point?
  • What is the best way to schedule my production?
  • How to balance the work load?
  • How to control the quality of the product ?
  • What is the optimal level of Inventories to be held?
  • What deployment strategy should be followed?(push vs pull)
  • What is the best control policy ?
  • What shipment size would be best (consolidated bulk vs lot for lot)?
  • What route to be follow for the shipment?
  • How shall I schedule my shipment ?
  • Who are my target customers?
  • Which brands should I focus on ?
  • How shall I beat my competitor?
  • Are the target customers cost sensitive ?
  • How brand loyal are my customers?
  • How effective are the campaigns and advertisement?
  • What is the Point of Sale ?
  • How to maximize my Portfolio?
  • How to maximize the yield?
  • How effective are the markdowns?
  • Whom can I target to cross-sell the profitable items?
  • How can I increase the efficiency of my sales force?
  • What is the expected demand at a certain point of time?
Analytics Offerings:After saying all this, I will divide the Analytics Offerings in various categories.

1. Supply Chain Analytics
  • Vendor Efficiency Model
  • Inventory Turns and stock levels
  • Fill Rates and stock outs
  • Storage Utilization
  • Network Utilization and Zone Routing
  • Inbound and outbound truck load Analysis
  • SKU Velocity Analysis
2. Store Analytics
  • Store Layout Analysis
  • Sales and Margin Rates
  • Shrink Analysis
  • Return Rates and Fraud Analysis
  • Credit Card Fault Detection
3. Campaign Analytics
  • Campaign Effectiveness Analysis
  • Channel Optimization
  • RFM Analysis
  • Mark Down Elasticity
4. Financial Analytics
  • Financial Fore-casting and Budgetary Analysis
  • ROC and Working Capital Management
  • Shareholder Metrics
5. Merchandising Analytics
  • Assortment Optimization
  • Product Pricing
  • Seasonal Trends
  • Category Contribution
  • Hot Items Listing
6. Customer Analytics
  • Customer Life Time Value
  • Profitability Analysis
  • Customer Segmentation
  • Market Basket Analysis
  • Cross-sell Options
  • Brand Switching and Loyalty Metrics
7. E-Business and Web Analytics
  • Click Stream Analysis
  • Web Sales and Traffic Rates
  • Subscription Rates
  • Store Cannibalization Analysis
  • Web Traffic Segmentation and Target Marketing
  • ROI and Web Spend Effectiveness Analysis
Result of Analytics Usage:Analytics can help to get good results but only when there is proper implementation and co-ordination among all stake holders. And most importantly, Analytics comes at much later stage of Vertical Solutions.

I would recommend three steps in process improvement.
1. First get the process right. Automate the basic operations and implement best in class IT Solutions. ERP Software Implementation is best.
2. Create Good Reporting System (MIS). Know everything what is going on in your organization.
3. Analyze how things can be improved to become the best in the market.

Analytics helps in getting closer view of three stakeholders of Business.
1. Customers : Know you customers
2. Competitors : know your competitors and their moves
3. Capital : Know yourself, your strengths and capability.

I will be frank with you here. After doing all this, what you can get from Analytics is the last 10% value add. First 90% lies with the first two steps . ... ERP and MIS.

Note : This article is originally published in Business Analytics Blog.  Summary of Analytic Offerings here.

December 19, 2009

Bernanke - TIME 2009 Person of the Year ?

A few days back, TIME announced Ben Bernanke as their 2009 Person of the Year.

I think Mr. Bernanke deserves credit for working diligently to prevent a total meltdown of the global financial system - all the time wearing a cool look and a calm demeanor.

But in fact, he did not do much in 2009.  The year started with the target Federal Funds rate at 0 - 0.25% and will most likely end the year at the same number.  And to take it a bit further, the Fed's steady stance in 2009 can be seen as abetting some other bubbles that will become more noticeable in the next few years.  The system is flush with liquidity (although banks are still not lending for fear of defaults - a lesson they have learnt too well), asset prices are rising, and bank's that were bailed out by taxpayers money have repaid their TARP dues so that bankers can get their fat end-of-year bonuses - although the very folks who bailed out these bankers out are sitting on unemployment rates of 15% or so (counting the discouraged workers, part-time employed workers, etc that are not included in the official unemployment statistics).

And he did not do much to fix the fundamental issue that was in fact the whole raison d'ĂȘtre of the crisis - the concept of "Too Big to Fail"; which essentially implied that bankers could make all the money they wanted in good times but could depend on tax-payer sponsored bail-outs in bad times.  This encouraged the kind of behavior that I have eluded to before.

No doubt, Mr Bernanke has this as his top priority for the next few months and I expect to see a few changes that will have far-reaching implications;  I only wish TIME has waited a year or so before making him their choice.  There were definitely others in 2009 who deserved this more.

Not Your Regular BI

The catch (all) phrase:

One of the frequently used buzz words tossed about lightly in client meetings and in general when someone’s trying to impress company is “analytics”. The sheer versatility of the term is also why it is least clearly understood – since it could be anywhere in the continuum starting from cleaning and presenting raw data in a form amenable to further number crunching; to canned reports or metrics presented in a glitzy dashboard; to sophisticated predictive statistical models and optimization. Advanced analytics is mostly about the third part of this spectrum. While we’re on analytics, let’s not even venture into the area of Google-based research producing voluminous essays, which also goes under the guise of analytics.
Gartner has listed advanced analytics as number 2 in the Top 10 strategic initiatives for 2010, based on conversations with CIOs, hype cycles and specific interest generated in the market. IDC’s prediction for predictive analytics tools shows a compounded growth of 8 percent over the next 5 years.
The proliferation of boutique firms with highly specialized industry expertise, and the spate of M&A activity, both point to the continued growth prospects of advanced analytics. While in the service oriented segment, there are a few cases showing loss of focus after a niche firm has been acquired by a larger diversified firm, fact remains that in the products space consolidation is the name of the game, leading to bigger and hopefully better advanced analytics products with significant market reach – IBM taking over SPSS should give SAS the big blues, no doubt. Things have clearly moved past the exploration and interest phase for analytics.

What’s the catch?

It is usually difficult for buyers to see through the marketing haze, especially with a myriad of solutions that claim to do everything in the analytics space. It doesn’t help either, that the term analytics is so ambiguously used to cover a huge gamut of solutions.

The need:

The need for each of these does exist, for different firms depending on the maturity of the firm in using data for “competing on analytics”, as Tom Davenport puts it.
Business users need accurate, clean and timely data to take good decisions. This is usually the grunt work that most in-house teams hate, but can’t avoid. They may use the IT department for generating data because the servers are in IT’s custody, but IT may not have the best view of what the business wants. Worse, business users may do it within their own team, wasting the time of expensive resources whose main work is held up because of reporting duties best handled by specialists with data management skills. Without good usable data, the promise of analytics falls flat, so while this is step critical, it is important that the right people for the job do it, so that business users can focus on what they do best.

In better cases, periodic reporting has been automated to a certain extent, but the usability suffers either due to lack of visual appeal or due to security and licensing restrictions. Some product companies have made a killing with potential clients, with visual impact scoring over analytical abilities of their competition. Visual data exploration has an exemplary proponent in Hans Rosling whose TED talks on macro economic data analysis and insights are legendary.
While everyone may not be have Rosling’s gift, the best are already there, going further than interactive graphs, going the whole hog using sophisticated tools to build statistical data crunching models. Banks have been at it for years. So have retailers and insurance companies, not to mention sports franchises, hospitality majors or your friendly neighborhood airlines jacking up the rates at will. The Holy Grail lies in the accuracy of predicting outcomes – be it finding out who will respond to a 10% discount offer; who will win the 2010 Super Bowl; what interest rate John Doe deserves for his new auto loan; or what inventory levels of each SKU should be kept to balance inventory turns and stock-outs.
And that calls for some wizardry, in the form of domain knowledge coupled with a thorough understanding of data management and statistical analysis techniques. If it had been that elementary, dear Watson, everyone would’ve been there.

One size doesn’t fit all

The time is overdue for every firm to do an objective evaluation of its situation, pressing analytical needs that would help solve business issues better, balance the short term and long term goals, and take the right step forward in the direction of advanced analytics. The competitive advantage offered by advanced analytics is officially up for grabs to anyone who is interested, has data and a culture that encourages fact-based decision making.

December 8, 2009

Webinar: A primer on Predictive Analytics

Please click here to download the presentation from the Dec8 webinar - "Getting your first Predictive Model up and running".

If you were not able to attend the webinar, you can find the recording here. Please feel free to view it and share it with your colleagues.