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
Techaisle Analyst Insights
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
This is a snapshot of data from the recently-completed "SMB Business Intelligence Solutions - Adoption and Trends".
Coverage includes SMB Primary Research conducted in US, UK, Germany, Australia, China, India to understand the current and planned adoption of business intelligence solutions, types of solutions including cloud-based, features desired, as well as barriers to adoption. This data only covers a slice of the US market. Click through - detailed charts are best viewed full screen:
SMB Business Intelligence Importance from Techaisle on Vimeo.
