$ 50 billion. That’s roughly how much marketers spend on big data and advanced analytics (according to a BMO Capital Markets Report) in the hope of improving the impact of marketing on the business.
This commitment reflects the belief that big data and advanced analytics can transform the business. While at times the promise has not lived up to the reality, some companies are already seeing significant value. Recent academic research has found that companies that have integrated data and analytics into their operations have productivity rates 5-6% higher than their peers. Now is the time to define a pragmatic approach to big data and advanced analytics, anchored in performance and focused on impact (see âMake advanced analytics work for you. â)
Here are four âfrontlineâ stories that illustrate how companies have used advanced analytics to make an impact.
1. Ask the right questions
The more data your business has, the more important it is to ask the right questions early in the analytics process. This is because the sheer scale of the data makes it easy to get lost or trapped in endless rounds of analysis. The right questions should identify the specific decisions that the data and analytics will support to drive positive business impact. Asking two simple questions, for example, helped a well-known insurer find a way to increase sales without increasing their marketing budget: first, how much should be invested in marketing, and second, to which channels, vehicles and messages should be invested. will the investment be allocated? These clear markers guided the company in its triangulation between three data sources, helping it develop a proprietary model to optimize spend across all channels at the zip code level. (For more on this, read “What you need to make big data work: The pencil“).
2. Be creative with what you have
More data can refine patterns of consumer behavior, allowing for a more accurate view of opportunities and risks. An emerging-market telecommunications company has acknowledged that its data could solve a long-standing dilemma facing financial services companies: how to meet the needs of millions of low-income people for revolving credit, similar to credit cards, without credit risk model. Telecom executives realized that their mobile network’s payment histories could be used as a way to solve this conundrum. Using this data, the company created an innovative risk model that could assess a potential client’s ability to repay their loans. Today, the company is exploring a whole new line of consumer finance in emerging markets that uses these insights as a critical asset.
3. Optimize spending and impact across all channels
Business is about trade-offs: price versus volume, cost of inventory versus risk of out-of-stock. In the past, many of these compromises have been made with little data and a lot of instinct. Even now, in the age of cookies and clicks, it’s not always easy to optimize spending allowances. Big data and advanced analytics – especially more real-time data – can take a lot of the guesswork out of it. A transnational communications company had invested heavily in traditional media to improve brand recognition and had invested in social media as well. However, its traditional marketing mix models could not measure the business impact of the social buzz.
The combination of data from traditional media, sales and customer usage of major social media sites resulted in a model that demonstrated social media to have a much bigger impact than strategists assumed. of the company. More critically, the company’s analysts found that the main driver of sentiment on social media was not its TV ads, but customer interaction with the company’s call centers. . By reallocating some media spending to improve call center satisfaction, the company dramatically increased its customer base and earned millions of dollars in revenue. (Learn more about this topic “Go beyond the buzz. “
4. â¨Keep it simpleâ¨
Too much information is overwhelming. This is why it is important to keep the reports simple or they will not be used. A large B2B manufacturer, for example, acknowledged that a significant percentage of the company’s sales came from a small portion of its customer base, but sales growth with those large customers was slow. Officials wanted local salespeople to find new customers. The company therefore created a central analysis team that collected detailed data and built predictive models identifying local markets with the highest potential to sell new customers. Rather than giving sales reps tons of complex data and models, the team created a powerful tool with a simple visual interface that identified potential new customers by zip code. This tool allowed District Sales Managers to see zip codes where there was a strong growth opportunity and deploy their sales teams to those areas. In the end, the use of the tool allowed the company to double its sales growth rate while reducing its selling costs. (To learn more, read “Use big data to find new micro-markets” and “Simplify Big Data – or it’ll be useless for sales“).
“The key to Big Data is to keep it simple”
Analyzing data isn’t an automatic ticket to success, nor is building a website that has turned every dotcom-era business into an e-commerce juggernaut. If the deployment of IT in the corporate world over the past 30 years has taught a lesson, it’s that adopting transformative technology still requires careful and creative management based on facts. The new new thing never succeeds without a lot of help from the old thing.
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