Techaisle Analyst Insights

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

Midmarket firms that adopt a holistic organization-wide digital transformation strategy are growing at 2.2X vs. Siloed digital transformation strategy. They are also experiencing 2.1X business process cost reduction, 1.9X better customer intimacy and 1.4X improved employee productivity vs. Siloed adopters. Techaisle’s US midmarket digital transformation trends study shows that it pays to have an organization-wide, holistic digital transformation strategy. Survey of 876 US midmarket firms reveals that Holistic adopters are experiencing better business outcomes than Siloed adopters of digital transformation.

We are all responsible for the pace of change – and to ensuring that it benefits rather than threatens our success. Nowhere is this clearer than with digital transformation – the adoption of digital infrastructure as the foundation for digital business processes, which enhance operational efficiency, employee empowerment, product innovation, customer intimacy, competitiveness and profitability throughout the organization. Businesses that embrace digitalization are more agile, more adept at using technology to accelerate cycle time and expand reach, better able to respond to market opportunities and requirements – while those that are left behind face an uncertain future in which one wrong step can lead to diminished business viability.

Holistic, Inclusive, Siloed and In-the-Shadows are the four midmarket segments by digital transformation strategy as revealed in Techaisle’s US midmarket digital transformation trends survey & segmentation data. The segmentation reveals that overall, 41% of the US midmarket firms (100-999 employee size) are firm believers in digital transformation. They are leading digital transformation initiatives. These firms belong to the “Holistic” segment of the four different digital transformation segments. They believe that digital technologies impact every aspect of the business and are a core part of organizational strategy. Interestingly though, within the firms belonging to the holistic segment, digitization of process automation is far from complete. They still have a huge runway in front of them.

For 59% of the midmarket firms, digital transformation initiatives are sporadic and ad hoc or not critical across the entire business. These are the firms that belong to the Inclusive, Siloed and In-the-Shadows segments. They are the laggards in digital transformation journey.

Clearly there is vast untapped potential for firms offering digital transformation services to the midmarket businesses.

Although much of the public debate around mobility involves hardware brands and feature sets and overall penetration rates, the real business benefit of mobility is delivered via applications that address specific task requirements within the business, and mobility solutions that overlay the management and security structures needed to integrate these apps with corporate IT systems.

Data drawn from the Techaisle 2017 SMB & Midmarket Mobility adoption survey shows that 2017 will see an explosion in the number of mobility application types used by US SMBs. The data presented in figure below shows that small businesses will go from a current average of seven mobility application categories in use to 14 in 2017, and midmarket firms will increase from an average of about six mobility app categories to 13. This 100%-ish growth pattern is demonstrated across most employee-size segments, with all but the 250-499 group anticipating a 2017 net increase in mobile app categories used of 86% or more.