CRM has become a core application for businesses and we have already seen that Sales Force Automation and Marketing Automation functions have been quickly incorporated along with Business Intelligence. All of these can use the same or linked tables to provide a 360 degree view of the sales and marketing process. However, today, we have finally come to a place where it should be easy enough for SMBs to plan and execute business strategy using a structured performance management system, like the Balanced Scorecard. Key Performance Indicators (KPIs) should be a standard part of the application architecture as should a meta-directory of KPIs that all applications can access. To measure the effectiveness of Sales, Marketing, Operations, and industry-specific activities, each area should have standard metrics and access to benchmark data that lets the SMB know how they are doing compared to peers, but rather than only using historical data it should be based on forward-looking objectives (leading indicators) that are tied directly or indirectly to activities designed to ultimately improve financial results. SMBs are seriously interested in measuring elusive objectives like Return on Marketing Investment, Optimal Pricing, Cost of Acquisition, Lifetime Customer Value. They want integrated applications that can not only measure these objectives but also be able to optimize effectively. This is what we call the Enterprise Performance Management (EPM).
For EPM applications to be really effective, they should be able to collect data from all applications and break into several areas; for people, productivity should be monitored through activity and results (as it already is in the new generation of SaaS applications), and effectiveness of software and equipment should be measured through algorithms that follow click paths, analyze application usage, optimize the process flow and usability of the systems. In some cases, like network optimization, filtering potential employees and ecommerce, systems should optimize themselves and human intervention should only be required when something is way outside the parameters defined by the administrator – who may increasingly be the LOB management.
With the EPM (Enterprise Performance Management) system SMBs will have a new attitude and culture that values and uses data visualization as the quickest way to gauge overall performance and specific areas of interest at a glance.
Most SMBs that have used CRM and ERP systems within the past few years are familiar with the dashboards that are available with many of these applications, either embedded or purchased separately. We believe that Dashboards will continue to evolve and be dynamic in several ways; the way they use data from subsystems like ecommerce and other real time feed sources, the way users can personalize the layout of their dashboards. Similarly, within the EPM, the actual KPIs should be dynamic and have the ability to build KPIs “on-the-fly” by calculating variables on the screen and saving the result in a meta-repository for all to use. It will have to become the norm.
While several SaaS vendors allow this kind of metric building and start the user at a dashboard, we have yet to see anything targeted to the mid-market or SMBs that connects the performance across front office, production, fulfillment and customer service. NetSuite does it to some extent almost out of the box. The market has to catch up. While this level of functionality is an excellent target, small businesses can probably get by with a good understanding of leads, opportunities, customers, invoicing, billing and customer service (or the appropriate subset) by integrating together several applications from different IT vendors. But the need for EPM is genuine and the industry has to quickly design solutions to empower SMBs with enterprise-level EPM technology at an affordable price.
Techaisle Analyst Insights
Global SMB Business Intelligence spend is estimated to be US$2.9 Billion in 2011, a little more than half of estimated spend by Enterprises at US$5.7 Billion. However, confusion abounds because of proliferation of front-end analytics tools and back-end Business Intelligence tools, analytical platforms, as well as data marts. And now more than ever the need for business intelligence is strong, especially among SMBs as they have to increasingly carry an added burden of managing, maintaining and developing insights from raw data.
Business Intelligence is among Top 5 investment solutions planned by SMBs. The current economic scenario has businesses of all sizes focused heavily on identifying profitable customers to improve the ROI on marketing dollars spent. While a number of SMBs have already deployed formal CRM solutions and many others have internally developed CRM processes, the next focus is on making sense of the data captured, linking it to business objectives and monitoring business performance. Large businesses have over the last decade spent billions in improving data analytics capabilities; however, typical business intelligence solutions have been out of reach for majority of SMBs due to cost and deployment complexity. But there are a host of new entrants in the field that are resetting the price bar and filling the gap between low-end MS Excel based solutions and high end solutions such as SAP Business Objects and IBM Cognos.
For example in the US alone, when Techaisle asked 850 SMBs:
Please tell us which of the following technologies you are either “investing in”, “investigating”, or “Ignoring”; [Investing: Have completed purchase, Post purchase deployment phase; Investigating: researching or in pilot phase; Ignoring: not considered important]
Results below for US SMBs shows that the market is ripe for growth and adoption.
Historically, businesses have used a hub-and-spoke model, that is, an enterprise-level data warehouse with dependent data marts. But this poses a problem as business intelligence and analytics are required by businesses to have high quality and incredible execution speeds because time-to-market is of essence.
As per Techaisle research, 50 percent of mid-market businesses (100-999 employees) and 53 percent of Enterprises (1000+) say that “Improving effectiveness of sales, marketing and business decision making through investments in data mining & business intelligence solutions” is critical. In such a dramatic scenario it becomes more useful for businesses to utilize a virtual data warehouse that pulls data dynamically from various applications as needed.
Similarly, on a scale of 1-9 where 9 is extremely critical, SMBs rate “Improving responsiveness to changing customer needs” as 6.5. These data points cannot be ignored.
Many upper-mid-market businesses use on an average of 6.1 different types of business intelligence solutions. These could be in-house development or a combination of SAS, IBM-Cognos, SAP Business Objects, Microstrategy, Oracle-Hyperion and several other players that provide point solutions. This leads to unclear KPIs, conflicting dashboards and only few metrics that are actionable. These mid-market businesses are trying to turn to analytics-as-a-service.
It would do well for vendors that are targeting the business intelligence to focus on analytics-as-a-service offering for SMBs. However, a key of aspect of any such solution would be the ability to quickly integrate applications or if not, ability to seamlessly pull data for the stakeholders in an easy to use format.
Tavishi Agrawal
Techaisle
In the thirty years from the time Shunu Sen posed the marketing-mix problems, I have been busy with marketing research. I tried modeling most of the studies and discovered that market research data alone is not amenable to statistical predictive modeling. Take for example, imagery. Is there a correlation between Image parameters and Purchase Intention scores? There should be. But rarely does one get more than a 0.35 correlation coefficient. Try and link awareness, imagery, intention to buy, product knowledge, brand equity, etc. to the performance of the brand in the market place and one discovers land mines, unanswered questions and inactionability.
This is where ANN steps in.
Technically ANN (Artificial Neural Networks) offers a number of advantages that statistical models do not. I will list a few of them.
- Non-linear models are a tremendous advantage to a modeler. The real world is non-linear and any linear model is a huge approximation.
- In a statistical model, the model gives the error and one can do precious little to decrease the error. In ANN one can specify the error tolerance. For example we can fit a model for 85, 90, 95 or 99% error. It requires some expertise to figure out whether there is an over fit and what is the optimum error one can accept.
- Statistical models make assumptions on distributions that are not real in the real world. ANNs make no distribution assumptions.
- Most ANN software available today do not identify the functions that are fitted. We, on the other hand, have been able to identify the functions that are fitted and how to extract the weights and build them into an algorithm.
How do we bring the differentiation?
Our biggest strength is in data integration that combines market research and economic data with transaction data into a single file. This is tricky and requires some ingenuity. We use Monte Carlo techniques to build these files and then use ANN for building the Simulation models. Optimization then becomes clear and straight forward since we do not use statistical models. Optimization using statistical modeling, which most modelers use, is a nightmare. Most of the large IT vendors and even analytics companies continue to use statistical modeling for Optimization. And therein lays the problem. Neither are these companies aware of the possibilities that ANN can provide. Most modeling is done using aggregate data, whereas we handle the data at the respondent level. The conventional modeling is macro data oriented whereas we are micro data oriented. Hence the possibilities that we can generate with micro data for modeling is huge, compared to macro data.
We have crossed the stage of theories. There are many projects that we have executed successfully that have gone on to become a must-have analytical marketing input mechanism.
Doc
Techaisle
It is always great to see someone who is really smart and knows what they are talking about...also kind of makes you wonder if your E-Trade account is up to the task and even if it is, whether it might be just like picking up pennies in front of an oncoming steamroller...click to view
