Mid-market companies must evolve if they want to remain competitive. Big Data is one of the solutions, if not the solution, to innovate.
“The company has two core functions, and only two: marketing and innovation. Marketing and innovation produce results, the rest is just expenses. Systematic innovation requires a willingness to see change as an opportunity. “(Peter Drucker) The Big Data, and especially the Big Data analysis, can carry this change.
Definition of mid-market
The mid-market is the market made up of companies with significant sales, ranging from $50 million to $1 billion. It is therefore a market straddling small companies and giants.
The adoption of Big Data by the mid-market
Mid-market companies should grow their IT department by 6.5% in 2016which is much faster than for other companies.
This major trend raises the question of whether mid-market companies will adopt Big Data faster than large companies. Cloud service providers have quickly realized that the mid-market is indeed the most relevant segment to target, it’s an easy market to contact due to its small structure. In addition, they are companies with fast processes, which reduces adoption time. As a result, Cloud providers have paid a lot of attention to mid-market companies, with innovative solutions that meet their needs and problems.
Mid-market companies have realized that it is increasingly crucial for them to take a data-centric approach to what they do. They see their data as a strategic lever for gleaning ideas, and using their available data to find ways to gain or maintain competitive advantage. Most pilot projects in the analysis of Big Data by mid-market companies focus on improving the quality of products or services (innovation) and the ability to take advantage of new market opportunities (marketing).
Big Data in Innovation
A large proportion of companies in the mid-market are involved in the design and production of industrial products or in the food industry. Many of these companies are digitally transforming their businesses, either to reduce costs, either to gain market share or to align their strategies with their main competitors. A large part of Big Data Analytics tools are used in the product development process.
Big Data Analytics also gives companies the ability to determine which products are selling and which are not, refine product strategy and provide objective evidence to support future product roadmaps. By evaluating support logs, product returns, and complaint data, companies can easily identify product issues, thereby make their business more profitable.
Big Data in Marketing
Historically, mid-market companies enjoyed large market shares in their niche segments with almost negligible marketing spend, a captive sales force and almost no online presence. In these companies, the sales department, more than in any other function, has long relied on the art of transaction.
In the past, the sales staff of these companies relied on relationships and soft factors to canvass and close deals. However, competitive pressure from domestic and foreign manufacturers with similar or comparable products is forcing organizations to re-examine their go-to-market and their sales approach. As a result, mid-market companies are interested in performance measurement tools that identify wealth-creating activities, products and markets, thereby eliminating unnecessary activities. The strength of marketing analysis is in the ease with which a campaign can be shown to generate revenue and profits, making the marketing department more agile, efficient and customer-focused.
Although many mid-market companies are just starting to enter the Big Data business, early results have been positive. an extremely positive impact on productivity and business success. These companies, in most sectors, believe that their businesses have everything to gain from effective Big Data management.
While mid-market companies may not have the same resources or budgets as their large corporate counterparts, they can take advantage of best-practices and avoid the pitfalls encountered by big business in taming Big Data.