Techaisle Blog

SMBs are being deluged with IT solutions that aim to address their pain points of reducing costs, improving sales and marketing, penetrating new markets, improving employee and group productivity as well as managing more IT with less. The channel comprising of SIs, VARs, SPs, MSPs and IT Consultants form the essential cogs of an IT vendor’s eco-system that puts products and solutions in the hands of the SMBs.

Today’s SMB channel has numerous vendors to partner with to build and grow its business especially if they are targeting the SMB segment. Each channel partner has usually has multiple vendor partnerships. It is therefore essential to have a positive mindshare of the channel which would potentially translate to wallet share.

Techaisle’s SMB Channel BES-360 provides an actionable path for IT vendors to manage their channels. Techaisle’s BES-360 Model looks at the equity of the brand on six overall independent dimensions:

1. Emotional,

1. Likeability,

1. Rational,

1. Dispositional,

1. Visibility, and

1. Human Connect

The data is collected by conducting a primary research and thereafter using ANN (Artificial Neural Networks) we model the responses on several variables with action variable using a non-linear model. Action variables are crucial to measuring brand equity, since having a brand equity which does not lead to action is useless. Techaisle’s BES 360 uses ANN for computing the dimensional weights as opposed to assigning arbitrary weights or no weights at all.

Cisco’s Brand Equity Score with SMB Channel Partners = 41

The model reveals that the BES of Cisco is 41 on a scale of 1-100. The question is, is this good or bad? Since the highest BES is 56, 41/56 is “Good”. Two other IT vendors including IBM have a higher BES than Cisco.



 

 

 

 

 

 

 

Breaking down the data for Cisco we find that almost 25 percent of Cisco’s channel partners have a BES of 80+. They form Cisco’s core partners. The customized report can delve deeper into the typical profile of these SMB channel partners of Cisco. The data also shows that almost 35 percent of Cisco’s SMB channel partners have equity of less than 40. These are the partners that Cisco needs to work with to try and raise the brand equity. Further research could also be conducted to check and see what these partners contribute to Cisco’s business and their relative importance.

If we look at Cisco’s equity among its own channel partners and non-partners, the difference in equity is substantial. The BES of Cisco among its partners is 55 and among non-partners the BES is 29. A polarised equity pushes a brand in to a niche status, which may not be desirable for all brands Cisco’s BES is the highest among the channel partners of Avaya and even among the channel partners of SAP too the BES is quite high. These could be potential channel partners for Cisco. 

 Techaisle’s BES-360: Why is Brand Equity Score Important?

Companies no longer produce products and services but deliver a brand experience through their products and services. It is widely recognized that the status of a brand in the mental space of the customer is best measured through brand equity. If the brand equity is good then a product or service that is similar to another brand with lower brand equity will sell better. Additionally, a brand with a good product or service but lower brand equity has a lower customer satisfaction compared to a brand with a higher brand equity that offers the same, if not an inferior, product or service. Hence measuring and tracking brand equity score is of critical importance to brand management.

What is the key information that I will get from Techaisle’s BES-360 to manage my brand?

Our customized report answers following nine relevant questions:

1. What is my BES and my competition in the industry?

1. What is my BES among my channel partners? Understanding overall equity is fine but this equity should also be good among its own channel partners and the difference between the equity among a vendor’s own channel partners and the non-partners should be significant. Otherwise it indicates a non-exploitation of the market completely.

1. What is my Brand Equity profile of my channel partners? The data and analysis provides critical information for assessing the potential for expanding the foot print of the brand to the other channel partners. The composition of the BES among the channel partners of a brand indicates the core strength of the brand. A brand needs to know what proportion of their customers are at, say, half the total BES? If a small portion of the channel partners have high brand equity and a large number have low brand equity then the customer base is shaky.

1. What is my BES among the partners of other channels?

1. What is the composition of my channel partners at various levels of BES? A brand would like to know the business that their partners generate at different levels of brand equity. For example: what is the number of solutions offered by a channel partner whose equity is twice the average brand equity? Such information can be quite useful to build a complete business strategy by better equity management.

1. How is the BES affecting my business among my channel partners?

1. What do I do to improve my brand equity? We measure brand equity on nine variables. We can dive and pick up the dimensions on which the brand needs to score. In fact we can even suggest using an optimization scheme the best values of the dimensions that the brand should achieve.

1. What business improvement do I expect at 5% increase in my brand equity from my channel partners? We can do a detailed analysis of our data to indicate what will be the impact of an increase of 5, 6, 7 or more points on the business, using the survey data.

1. Which brands’ partners should I choose to enlarge my foot print?

For SMBs, channel partners are the trusted advisors. Addressing the channel partners directly contributes to raising the brand equity among SMBs (measured separately by Techaisle). We call it BES-360 because it covers all the dimensions as well as competition.

If more information is needed for developing a comprehensive and successful marketing strategy Techaisle has the capability to provide the necessary information. From the current data itself we can get more information by looking at the scores on each of the nine variables. However, we can also do a dedicated BES 360 Survey for a specific brand and get a comprehensive picture of the brand that can identify and answer strategic questions like “Why my score is low on the VISIBILITY dimension and what should I do about it?”

 

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.

1. Non-linear models are a tremendous advantage to a modeler. The real world is non-linear and any linear model is a huge approximation.

1. 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.

1. Statistical models make assumptions on distributions that are not real in the real world. ANNs make no distribution assumptions.

1. 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

What is the market mix problem that I keep talking about that has stayed unsolved for so many years. The problem by itself is simple: How much does my brand sell for different marketing inputs? A regression model needs historical data of at least three years. Most companies either do not have that much data, or the markets have changed in three years and hence the data is pretty much useless. In addition, regression models cannot accommodate changes in product formulation or advertising campaign changes. Further, economic data like GDP, CPI and inflation can never be built into the regression model. Hence all statistical modeling leads you up a cul-de-sac.

So what did I do that was so different? I went back to market research data. Almost all companies do advertising or brand tracking. The sample sizes in these tracks vary from 800 to 4000, every month. The tracks capture primarily the exposure to media, awareness of advertising and the brands, brand imagery and Intention to buy. They set up benchmarks against each parameter and take marketing decisions. For example: if the claimed exposure to TV in April is 30% and it was 40% in March, they call the advertising agency and ask them to change the channels. If the imagery is not improving then they will change the advertising. These decisions are ad hoc, because they have no clue on how each of these decisions will affect a priori. The marketing guy’s knowledge is always from hindsight. So, what we did was to pick up the brand track data with 800 or 4000 respondents and modeled it. The basic modules were:

1. Exposure to Awareness Model

1. Awareness to Image Model

1. Image to Intention to buy Model

1. Intention to buy to actual sales model

With this approach we had solved one two problems: The requirement of long history of the brand’s performance and the irrelevance of historical data to the current scenario. We could take the brand track data from April and model it for May. Nothing can be more recent than this.

Now came the tricky part. How does one link media and other marketing inputs. This was done through a heuristic algorithm. Each respondent in the track was assigned a value for each marketing input. Now we had a file with track and inputs in each record. After this it was easy to integrate all other data. Once we had all the data together the independent modules were built using ANN and a dash-board was designed to make life easy for the user.

What were the results? Based on each month’s inputs we estimated the sales. What we could deliver was 85-95% accuracy of forecasts compared to the company’s sales-out figures. This far exceeded the original mandate to deliver a solution that gave 75% accuracy.

How does this help the brand managers? Here is a list.

1. Understanding of the brand characteristics using the tabulator cuts down the time of extracting information from different sources by 80%.

1. Simulate the brand response to variations in brand inputs to get the optimum response.

1. If there is a competition activity in the market it is possible to check what investments should they make to reduce the effect of these activities on their own brand.

1. Use the simulator during brand and advertising reviews.

Our Brand Modeling solution is a huge help in effective planning of a brand to meet the targets. With our unique Brand Modeling Solution a 30-year-old problem that brand marketers have been grappling with was put to rest. We have a successful solution.

Dr. Cooram Ramacharlu Sridhar (Doc)
Techaisle

Before you attempt any modelling you should first look at the inputs and outputs that you want to go in to your modelling. Here is the matrix:

What you need to do is to make a laundry list of the variables (inputs) that affect the output. Typically in a marketing company one would look at sales as the output and a whole lot of variables as inputs. Let me look at a few examples for these cells.

1.       Measurable-Controllable Variables

GRPs of your brand through TV advertising are measurable and controllable.

2.       Measurable-Not-Controllable

Inflation is measurable but not controllable

 3. Not-measurable – Not Controllable

The amount of investments made by your competition in dealer incentives is neither easy to measure accurately nor can you have any control. But this activity impacts the sales of your brand.

4. Not Measurable-Controllable

Not measurable generally refers to qualitative issues which are quite often measured by a pseudo variable, for example: Quality of your salesperson.

In your business environment if the majority of your input variables are in Cells 1 and 2, and you feel that these make a big impact, then modelling will be successful. If not, and many variables are in Cells 3 and 4, modelling will not be a success.

Most companies do not undertake this simple preliminary exercise of classifying the variables that impact their business and then hit potholes throughout the design testing and implementation.

Unclassified variables are veritable landmines. Watch out for them.

Dr. Cooram Ramacharlu Sridhar (Doc)
Techaisle