I am often asked how Business Intelligence can help one’s organization measure sales channel effectiveness, sales team effectiveness, lead to sales insight and insight into sales process. This blog is dedicated to all such customers and all others interested in knowing a) why they need Sales Performance Management, b) how one can achieve it and c) what can be measured.

Lets start with a quick poll

Sales like every other department in a company wants to measure how they are doing it, why are they doing it this way, and how can they do it better. Sales managers wants to see how their reps are working and CSOs wants to see how their RMs are working, who bought how much revenue, in how much time, with how much expense incurred etc. In today’s market with never-seen-before increase in competition, no company can afford to stay on top of game.

Organizations often suffer from missed sales quota, increasing staff turnover, slower revenue growth, decreasing customer satisfaction and loyalty, slower customer acquisition, smaller sales pipeline, fewer sales per salesperson, insufficient account penetration, lost revenue opportunities, missed revenue targets, poor pipeline management and slower close rate. Auditing and verification of prospects and expected revenue often stays out of trust, sales numbers are often pumped up, and often faked many times. Declining revenue and low sales productivity, new competition, high dependencies on too few customers, dissatisfied major customer, dropping market share and growth rates below industry levels are few out of many challenges that sales organizations often face

Companies often kicks in sales training programs, hiring of more sales force, reorganization of the team and many other measures which sometime may help to solve the problem but only to a little depth prove to be a weak exit strategy. It is more important for Sales manager or CSO to know exactly what their team is up to and have a complete transparency and insight into sales operations.

Data (information) led driven initiatives and decisions could help organization understand and zero down to exact problem and guide them on the best possible measure to rectify It. Business Intelligence in sales could help organizations improve reporting, sales forecasting, provide insight into sales operations, enhance sales management capabilities.

Let’s have a look at the A2Z of sales performance and see what can be measured to know all (at least most) about sales operations and enhance sales effectiveness:

A. Sales by region, channel, product

B. Profit  by region, channel, product

C. Profit margin %  by region, channel, product

D. Pipeline by age, stage(potential lead, short list, selected, closed), type, channel

E. Wins by size, revenue, industry

F. Sales cycle for wins(prospecting, qualification, need analysis, value proposition, identify decision makers, perception analysis, proposal/ price quote, negotiation/review, closed won)

G. Lead Responsiveness

H. Average deal closing time

I. Operating cost and expenses incurred

J. After sales support revenue

K. Customers acquired (by product, branch, region, customer segment)

L. New product activation

M. Target achievement (by region, branch, customer segment, product)

N. Revenue per month (by product, branch, region, customer segment)

O. Expenses Incurred (by client, region, month, customer segment, branch)

P. Pipeline analysis (new customer analysis, stage wise aging analysis, health check for new accounts)

Q. Client satisfaction

R. Number of repeated customer

S. New customer

T. Sales forecasting (risk mitigation, predictable revenue, sales forecasting)

U. Pre mapped plan Vs. achieved results

V. Targets Vs. achieved (by product, branch, region, customer segment)

W. Lead closure ratio

X. Client meeting effectiveness

Y. Sales team analytics (customer acquisition, product sales, book growth)

Z. Sales Funnel Analysis (new opportunity, initial communication, fact finding, develop solution, propose solution, solution evaluation, negotiation, purchase order, account maintenance)

Information led decision-making could enable accurate sales forecasting, early identification of cross-sell and up-sell opportunities, insight into sales operations, faster and smarter decisions, help companies close deals faster and help increasing sales volume in a shorter period of time.

‘You can’t fix you do not measure’

-Abhinav Abhinav

I have recently started to hear about Prism, which utilizes Column storage and this ‘cool’ just-in-time in-memory processing it has got. This weekend, I got a chance to play around and do some basic research on it. Here are my first few observations.

Components

Visualization

Web demo provided on their website looked neat, clear and seemed like an easy to read interface. I found Prism demo less crowded with basic colors but still very readable or as they like to call it ‘intuitive’. Seemed like an interface for efficiency or as windows phone commercials say it: Get-in, Get-out. I liked the ability to highlight, add subtotals, sort visualizations etc on the go for better understanding.

In addition to filters the ability to filter out all the visualizations based on selection on one, helps to quickly analyze and understand relationships on the go. Although for now, it seems selections made in visualizations can be changed only by clicking at a little icon that pops when you hover your mouse over that visualization. But sometimes it can be difficult to figure out where selection has been made and you would have to hover over each and every chart and click to remove selection. I have not looked much into it but it might already have another way to remove selections.

Engine

“Prism stores all the data it processes in ElasticCube data repositories, which are column-based data stores containing the unified data of all combined data sources.” 

“Prism performs all query processing on data which is loaded into memory only when it is needed for a query”. My concern to this just-in-time in-memory is the performance compromise on this addition step of loading data every time however it does mean no more compressing and loading of all data and in-memory saving tons on hardware and need to reload the whole data if there are any additions or changes in data model.

“Prism’s data storage and handling is based on an “elastic data structure,” ….. Virtual data merging and multi-source data abstraction are performed automatically by the software, allowing the user to create “data mash-ups” across data from multiple sources, effortlessly.”

Goods

• Good price-quality ratio
• Scalability
• Self-service, no programming or scripting (SQL scripting available if desired), drag and drop report creation
• Query against of data files,  ODBC-compliant databases, OLAP cubes and cloud data sources
• Column storage based, high performance central data repository
• Zero foot print web browser-based analysis

Although it does lack the ‘sticky’ feeling and might need more visualizations to be added to stack but no doubt Sisense has thought clear and far enough to put together a tool with a solid foundation and is equipped with latest technology to handle scalability and performance.

Today in the world of data driven businesses and need for access to right information in right time logistics and transportation industries need query and reporting solutions to access critical business information for strategic decision making.

Business drivers in transportation include:

1. Increasing competitiveness in market
2. Unpredictable market trends
3. Cost cutting
4. Need for granular visibility into dollar spending
5. Trends in cost and performance
6. Root cause analysis
7. ‘What if’ scenarios for resources

Few of the areas that BI can impact in Logistics and transportation industry are:

Some of the KPIs worth measuring are:

1. Carrier Management (planning, capacity utilization, resource utilization, load balancing)

2. Route Management (lane utilization, scheduling accuracy, line haul efficiency, and planning and deployment efficiency)

2. Terminal Management (dock productivity, vehicle load/unload time, dispatch operations, labor efficiency, carrier selection, on-time vehicle arrival, order receipt accuracy, percentage of goods damaged, damages as a % of throughput)

3. Productivity (load efficiency, on-time delivery %, average transit time, cost of transportation, empty miles)

4. Financial (Net revenue per terminal, fuel surcharge per terminal, fright bill audit, bill processing, payment service, total transportation cost as a % of delivered sales)

5. Order Analysis (order receipt time, order information accuracy, revenue per order, percent order discount, cancellations, returns, fulfillment %)

Regardless of size, 80,000 or 80 employees, 2 million dollars or 2,000 million in revenue; almost every company suffers the pain on multiple versions of truth, inability to perform in-depth analysis, inability to locate important information, inability to differentiate and prioritize problems, nonalignment of operations with strategic goals and data governance issues.

Belief that mid-size companies cannot afford and handle Business Intelligence. Small and mid-size companies which do not have vast resources are inclined more towards a complete, less expensive easier, fast-to-deploy-and-administer applications. They need to be agile and quickly respond to changing market conditions

Most of Business intelligence applications targeting this sector of market offers integrated planning, reporting and analysis solution e.g. IBM Cognos Express, it targets specifically mid-size companies and provides them an integrated package consisting of Reporter(Reporting), Advisor(Visualization), Xcelerator(Excel based analysis) and Planner(Planning), Cognos express provides capabilities of both Cognos analytics and Cognos TM1. Others in the same category are SAP Business Object,  SAS etc. Some of other smaller BI vendors that target small and mid-size business market include Bitam, LogiXML, Targit and Dimensional Insight. Qlikview that held strong in mid-market until recently and now can also been seen getting adapted in much bigger enterprise environment.

Quick deployment, low-cost and ability to use  external business knowledge and technology makes SAAS also as a good option for small and midsize companies. Microstrategy, one of the largest pure BI vendor, is soon coming up with Microstrategy Cloud Intelligence offering, which is in Beta testing right now enabling its BI software platform available as a service in the cloud. Good thing about SAAS is that the vendor hosts and maintains the entire BI environment including servers, operating system, BI application and network and small and mid-size companies can enjoy the elasticity and scale their implantation if needed.

Another considerations while picking up the vendor/tool is the license cost; is it server-based or on the number of users. Which model will give you that magic number that can you afford. Low cost vendor, Logixml gives you server license independent of number of users. If your company has lot more information consumers that generators that going for vendors that does not require a client license to be able to view the reports will prove a god option for you. Many vendors have now option where reports can be published over the web, these reports can be refreshed after every certain hour and can be viewed in a web browser.

Depending on the size and type of organization they could leverage using one or more following applications:

1. Planning Budgeting & Forecasting
   1. Planning
   2. Workforce Planning
   3. Capital Asset Planning
   4. Operational Planning
   5. Margin Planning
   6. Budgeting
   7. Services Planning
2. Business Intelligence and Performance Management
   1. Reporting
   2. Dashboarding
   3. OLAP
   4. Data Mining
   5. Real-time Decisions
   6. Predictive Analytics
3. Customer Relationship Management
   1. Channel Revenue Management
   2. Order Management
   3. Service
   4. Marketing
   5. Sales
   6. Partner Relationship Management
   7. Social CRM
4. Financial Management
   1. Accounts Payable
   2. Accounts Receivable
   3. Fixed Assets
   4. Asset Lifecycle Management
   5. Cash & Treasury Management
   6. Financial Control and Reporting
   7. Financial Analytics
   8. Governance, Risk & Compliance
   9. Travel & Expense Management
   10. Treasury Management
   11. Disclosure Management
   12. Financial Close Management
   13. Financial Data Quality Management
5. Governance, Risk and Compliance Management
   1. Financial Governor
   2. Risk Management
   3. Environmental Governance
   4. Fraud and Error Reduction
6. Supply Chain Management
   1. Procurement Management
   2. Asset Lifecycle Management
   3. Global Trade Management
   4. Manufacturing
   5. Order Fulfillment
   6. Product Lifecycle Management
   7. Logistics
7. Human Capital Management
   1. Human Resource
   2. Talent Management
   3. Workforce Management
   4. Workforce Service Delivery
8. Project Portfolio Management
1. Project Contracts
2. Project Grants
3. Project Billing
4. Project Costing
5. Project Resource Management

It was very likely that I would write on KPIs in Retail or Store Analytics since my last post on Marketing and Customer Analytics. The main motive behind retailers looking into BI is ‘customer’ and how they can quickly react to changes in customer demand, rather predict customer demand, remove wasteful spending by target marketing, exceeding customer expectation and hence improve customer retention.

I did a quick research on what companies have been using as a measure of performance in retail industry and compiled a list of KPIs that I would recommend for consideration.

Customer Analytics

Customer being the key for this industry it is important to segment customers especially for strategic campaigns and to develop relationships for maximum customer retention. Understanding customer requirements and dealing with ever-changing market conditions is the key for a retail industry to survive the competition.

• Average order size per transaction
• Average sales per transaction
• Average number of items per transaction
• Average profit per transaction
• Number of trips per month
• Average shopping time
• Visit to buy ratio

Financial Analytics

Sales analytics is key to track trends in product demand and opportunities in sales. Marketing analytics can help us track effect of campaigns in sales and help us target customers and plan for promotions.

• Total Sales
• Gross Profit Margin
• Price premium
• Actual expenses
• Total payable
• Total receivable
• Return on capital invested
• Margin %
• Markup %
• % of revenue generated from non-house brands
• % of profit generated from non-house brands
• % of revenue generated from house brands
• % of profit generated from house brands

Store Analytics

Monitoring store operations is vital to measure management efficiency and how campaigns are working regionally.

• Sales per hour
• Sales per labor hour
• # of transactions per hour
• Sales per m²
• # of products per m²
• Revenue/Profit per m²
• Store conversion rate
• % of returning customer
• Fixed cost of opening per month
• Variable cost of opening per hour
• KWH per square foot
• % of not displayed inventory
• Average time on shelf
• % of expired products
• % of damaged products
• % of returned products
• Average # of employees
• Wage to sales ratio
• Average Inventory value
• Inventory Turnover

Other areas BI can impact in retail include supplier management, budgeting, planning, supply chain, logistics, branding, social analytics, merchandise management, market analysis etc.

Making your next or current Business Intelligence initiative in your organization fail-proof by staying on top of latest Business Intelligence trends and technology and learning best practices from leaders in retail industry, and covering your bases on  are doing is the ‘key’ to avoid commonly faced challenges such as user adoption at later stages.

I have been thinking of writing on Marketing Analytics since the last round-table at BI-Exchange here in Pittsburgh hosted by Datavibes, where the topic was being presented by speaker from large health and nutrition product retailer. It was interesting to listen on how they manage their customer database and use lots of email marketing for campaigning and promotions.

While email marketing and loyalty card and social media has been picking up, it is important to know how customers have been reacting to it. As recommended  in Good marketing practices by Dr. Dave Schrader, Director Marketing and Strategy at Terradata,  Collect the ‘clues’ (purchases, browsing history, calls to call center, responses to marketing campaigns, survey responses etc). Then use these ‘clues’ to make customized offers.  He also emphasizes that fewer-but-relevant messages is all needed for higher conversion rate and satisfaction. So what all clues or in technical words KPIs should we be monitoring while planning or measuring success of our Marketing strategy? I think like most other BI recommendations there is no direct answer or ‘n’ KPIs that can give you an answer. It really depends on lot of different factors like company structure, presence, marketing channels etc. In some cases a lot can be achieved just by monitoring few KPIs and in others advanced tools like basket analysis or statistical techniques like predictive analytics are required for effective Marketing analytics. Some of the common KPIs that we can possibly capture or mine using all this data for planning, development, execution, management, monitoring or collectively MPM (Marketing performance measurement and management) are:

• Response rate
• Lead to pipeline ratio
• Lead to closed sales ratio
• Inquiry growth following campaign
• Lead to pipeline conversion
• Marketing cost per lead per segment
• % of leads generated
• Marketing budget ratio
• Return on Marketing Investment (ROMI)
• Contact rate
• Effective reach (% of targeted-audience)
• …etc.

What can we expect to achieve using Marketing analytics?

• Demographical demand analysis
• Direct Marketing
• Lead Generation
• Cross and Up-Sell
• Customer Insight
• Loyalty analysis
• Customers buying pattern
• Promotions effectiveness
• Product/Service Analytics
• Pricing and sales modeling
• Coupon conversion percentage
• Store conversion rate
• …etc.

It is important to consider all the channels while running marketing analytics (Tele Marketing, Direct Mail, Emails, Trade shows, Organic Traffic, Social Media etc.).

Email Marketing is no more sending promotions or newsletter. It has rather become ‘preference driven’. Netflix and Pandora are two of my favorite examples when talking about preference driven options. Companies have similarly been using analytics on email marketing analyzing factors like what links were clicked, click to sales conversion, customer patters, cross sell etc. Some of the email campaigns that are actively used are:

• Active Subscribers
• Welcome campaigns
• Preference campaigns
• Birthday campaigns
• Holiday campaigns
• Special promotion campaigns
• Reactive campaigns

Rewards Programs or Loyalty Cards are win-win for both company and customers. Credit card companies and Airlines were the first ones to start these and soon taken over by many others like car rentals, hotels, super markets, grocery chains, retailers, restaurant chains to anything, even the smallest local coffee shop or spa  in your neighborhood where you can get your nth coffee or haircut for free.  Companies can learn about purchasing patterns and cross purchasing while customer get rewarded for their loyalty to stick with the same company. Some of the KPI that can be are:

• Conversion rate
• Response rate
• Time taken to reach reward
• Rewards used

With the popularity of facebok, twitter, linkedin etc. Social Media Marketing has been picking up. You can now find your favorite airlines, bands, retailers or events like concerts or conferences  etc on these sites advertising few extra miles or discount coupon when you subscribe or ‘like’ their page. Why is it all this hype about followers on social media? These platforms are not just an interaction medium but really is a huge customer database but now the question that lies in front of everyone is how to use it. One of my favorite example from the Microstrategy’s CEO Michael Saylor at his keynote at Mircostrategy World this February in Las Vegas was of I-phone/Android app by a wedding dress retailer that taps into Facebook data for relationship status changes to Engaged, send targeted customized messages with promotions and have an option to update the status for bridesmaids to register at website for further discounts on dresses. If you really think there is lot that can be done with the huge customer data that has been accumulating everyday. Some of the KPIs that can be used for social media marketing are:

• Message reach
• # of tweets/mentions
• Ratio of positive comments
• # of followers/fans
• Links to th sites
• Topic trends

When we talk of Marketing analytics, Customer Analytics is something that does not go untouched. Looking at customer behavior helps to make important business decision regarding direct marketing, geographic selection and CRM. In simple words, is it profitable to acquire or retain the customer or let it go. Affinity or Market Basket Analysis is one of the often asked advanced data analysis technique that is used to discover co-occurrence relationships among activities performed by specific individuals or group e.g. if you buy a certain group of items, you are more (or less) likely to buy another group of items. It can be used for cross-selling and up-selling, in addition to influencing sales promotions, loyalty programs, store design, and discount plans.

Please provide us feedback by taking the poll below and adding comment on this blog on how you are using marketing analytics within your organization.

Traditional BI technology loads data onto disk as modeled tables and multidimensional cubes, queries are then made against the tables and cubes on disk. Limitations of disk based (RDBMS or OLAP) techniques include performance limitations requiring intermediate aggregation tables, low flexibility to adapt to changing business needs, limited scope of analysis and long implementation cycles.

In-memory technology removes these steps, as data is loaded into RAM and queried in the application or database itself. This greatly increases query speed and lessens the amount of data modeling needed. Memory prices keep dropping which makes it economically viable to increase capacity for in memory processing. Faster performance on larger data sets with less data management seems like a win-win situation for the organizations.

While data warehouses guarantee integrity and provide a stable server environment for managing data, in-memory can make information accessible at the time it is needed and available to anyone who requires it.

“In-memory technology is not in and of itself a driver of BI growth, despite the massive hype of many vendors. To drive adoption, in-memory must be coupled with consumer-oriented BI tools, a combination that has been at the heart of data discovery tool success. As they have been to date, in-memory capabilities will continue to be an enabling technology. They will expand BI to a broader range of users, as more and more BI vendors incorporate it into their portfolios to deliver Google-like responses in exploring vast amounts of increasingly diverse data types via intuitive, yet sophisticated and mobile BI tools and applications.“, Gartner Jan 2011.

There are few articles I would recommend reading including Top 10 technical requirements your in-memory analytics vendor by James Mandrid, What to look for from you In-Memory BI platform by Qliktech and finally my favorite from Boris Evelson, at Forrester on Not All In-Memory Analytics Tools are created equal. Another interesting article you might enjoy is by Elad Esraeli where he talks about how In-Memory is not the future, It’s the past.

Qlikview, Tibco Spotfire, IBM Cognos TM1 (formerly Applix TM1), SAP BusinessObjects, MicroStrategy, Microsoft, Tableau are some of the in-memory vendors in the market. Personally I think in-memory is not the best option for scalable, multi-user BI apps. 64 bit computing on column based technologies can provide another alternative to hefty OLAP projects.

Why there is all the hype on in-memory BI?, Why it has become so fashionable?, How new and innovative it is?, Will it kill off disk based BI and the is th next breakthrough and What is the next breakthrough?. A report on What in-memory BI ‘revolution’? by Business Application Research Center (BARC) would answer most of your questions along these lines.

New addition to Gartner’s magic quadrant this year and such a low-cost BI vendor is LogiXML, based out of McClean, VA which if I remember right used to had 1-50 employees few weeks back on their Linkedin profile, recently changed to 50-200.

LogiXML has a single sold platform and attracts smaller to mid size deployments because of their of course much smaller cost as well as ease of use, rapid deployment and embeddability. As it is quoted in the Gartner survey “It is chosen more often than most other vendors for overall TCO, license cost, and implementation cost and effort“

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Microstrategy as a company have excellent support relationship and as a product one the best performance. It is also one the early adopter of Mobile strategy and personally I think have one of the best mobile strategy and interfaces. You can check out my notes from Microstrategy World 2011 I attended early this year, powerful technology they are working on and Microstrategy’s vision and keynotes.

Lets have a look at Microstrategy’s enterprise solution stack

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Real time analytic applications, ad-hoc analysis, what-of modeling, statistical and predictive analytics yet easy to use tool seems to getting popularity in the market giving ability to users to do basic to advanced analytics.

Spotfire products

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