Data is the raw material of the 21st century. It is left by users in rough quantities as “digital footprints” on their customer journey through the internet. Companies simply have to evaluate this “big data” to increase sales and customer loyalty. But does big data really help and is the whole thing really as easy as it sounds?
Why is big data important to marketing and what are its limits?
Companies expect to reap great benefits from evaluating the huge amounts of data collected on a daily basis by IT systems. The central question regarding the use of big data analysis in marketing is, “What’s the point?”. There are actually enormous advantages to using big data analysis, assuming one has direct or indirect access to the relevant data.
And this is not likely to get easier in an age of e-privacy policies, the coming EU General Data Protection Regulation and other unforeseeable laws governing data acquisition, storage and usage, which will all increase the requirements with regard to compliance.
If companies closely monitor their customers and prospects, and analyse the customer journey including the data, they will be able to more precisely address target groups and specific buyer personae. Companies can therefore make their digital messages much more targeted. This serves to minimize loss and thus save costs in marketing and sales.
Increasing conversion rates
Cross-selling potential can be recognized by linking data to user behaviour. Therefore, new sales incentives can be achieved using a targeted sales approach when the appropriate messages are sent at the right times in the right places.
Analysis of the market and the competition
Big data can be evaluated to monitor the competition and derive important conclusions. Companies can analyse this data, for example, to determine success factors and their own market position. Overall, big data can provide companies with a solid foundation that they can use to adapt their own strategy and plan of action to gain an advantage over the competition.
Strengthening customer loyalty
Intelligent data analysis can not only be used to attract new customers. Continuous analysis makes it possible to identify tendencies of existing customers to go elsewhere and counteract this using appropriate measures such as retargeting. On the other hand, data-driven customer relationship management can help retain existing customers and stimulate them to make subsequent purchases or recommend the company to others.
Marketing Data Scientists help achieve transparency
To be able to take advantage of big data, it is not enough to just collect large quantities of data. After all, not just any information that can be collected is actually helpful for a company’s marketing activities. The key consists of converting big data into smart data.
This takes experts who know exactly which messages and channels of communication can be used in the dialogue with the customer to support targeted marketing. Marketing Data Scientists can play an important role in this regard by making sense of data chaos. Most of the information, after all, is not structured or only gains relevance when used in combination with other data. Marketing Data Scientists help sift through the data and extract the facts relevant to the company. Proper analysis results can be used to create quite specific behaviour profiles, which reflect the interests, inclinations and needs of potential customers. Companies can use this to derive tailor-made offers and customize their communication. Marketers can now “get to know” their customers digitally and communicate with them accordingly.
AI marketing will become essential
Even using the expertise of Marketing Data Scientists, however, it is often impossible to meaningfully evaluate the huge amounts of data already produced by internet users either manually or using conventional IT systems. In light of the flood of information and the increasing effort required for analysis, many companies take a sceptical stance toward the use of big data. Do the benefits justify the costs? This is quite a dilemma, because big data usage is seen by many business decision makers as a critical success factor for the future.
Artificial intelligence (AI) offers a growing number of solutions to this dilemma. AI systems are already capable of evaluating large masses of data based on specific requirements and can provide Marketing Data Scientists a good foundation for making decisions.
That is why AI and big data are at the top of the list for 345 top marketing experts according to a recent study. The provider of cloud-based marketing solutions Tune showed that 37.4 percent of the people it surveyed view artificial intelligence, machine learning and deep learning to be the most disruptive technologies for 2018, followed by big data and personalization (16.8 percent). This is also reflected in the planning of many companies. According to the study “AI Marketing Readiness in Retail and e-Commerce” conducted by Emarsys, a provider of B2C marketing cloud solutions, 86% of the respondents were convinced that they can use artificial intelligence to make their marketing more efficient and effective. 78% said they want to increase their budget for AI marketing technologies by at least 5% within the next 12 months. However, 65% indicated that they do not have the necessary knowledge in the area of product management to successfully implement AI marketing.
Often still in the starting blocks
In other words, only a few companies are already strategically and organizationally ready to use innovations based on artificial intelligence in marketing and to effectively improve their business results. However, this is bound to change due to the rapid technological development. Big data in conjunction with artificial intelligence will most certainly catapult the marketing of the future into new dimensions. Of course, this requires access to big data to not be too heavily regulated in the next few years.
Nonetheless, the development is forcing companies to quickly hone the in-house expertise to first explore the relevance of the technical developments for their own business models and then – when appropriate – to achieve value-added use of AI in marketing using prototypical applications.
Find out more about how companies are already using artificial intelligence for digital marketing here.