Marketing automation : sales must become smarter

New technologies such as Big Data and AI help companies create a better customer experience. How to best implement the technologies for distribution and marketing automation? How to avoid increasing the number of campaigns while impact is absent?

13.11.2018

In this expert note, Jean-Jacques Pittet – Head of Business Consulting and Bertil Maire – Head of Customer interaction management at ELCA describe the impact of new technologies such as Big Data and AI in marketing automation to help companies create a better customer experience.

by Jean-Jacques Pittet
Head of Business Consulting

Three central questions need to be addressed to ensure the success of marketing automation using AI or other technologies: What is the biggest challenge for your company, your distribution organization or your customer relationship team – and can automation really help them? Which methods, data and technologies do you need? Have you established the basic prerequisites for the deployment of these technologies?

by Bertil Maire
Head of Business Line Customer interaction management at ELCA

At the same time, you have to ensure your customer relationship endures and customers continue to entrust you with their data. You must credibly and transparently ensure that the cyber risk is minimal and that you comply with regulations (e.g.,: Are you complying with DSGVO/GDPR or the upcoming Swiss equivalent?).

Applying AI appropriately

Big Data and AI have already been successfully deployed, e.g., in the refinement of customer segments, prediction of these customer groups’ needs, prediction of purchasing windows and freeing customer service personnel from routine tasks. Just these four examples show the economic relevance of the correct application of AI and other technologies.

A precise diagnosis of the business area as the starting point is important and easy to illustrate. Let’s assume you’re a retailer of goods or raw materials in a very seasonal, declining market. If you can launch your offer at the right time you’ll be more successful. Accurate prediction of that time is also a key element in your strategy and can result in bigger profits.

 An example? ELCA analyzed historical transaction ERPs – including publicly available data – for a large Swiss retailer in order to determine the next purchasing window for specified products for each customer. As the client was ready (e.g., required data quality achieved and a functioning distribution system available), we applied the most modern technologies and algorithms.

Not to be overlooked, however, is that just a few exchanges between the experts and data scientists led to a precise model and misinterpretations could be avoided.

The example shows that an organizational unit’s data quality, process effectiveness and systems determine which approach makes the most sense. Often, known measures such as an effective distribution system (CRM) or skillful use of available data (e.g., Big Data) solve the most important problems. If, however, one wishes to benefit from the most reliable prediction, or discover new business areas, one must deploy AI. Here, one must also be conscious that the demands on the organization will be greater.

A relevant example. New technologies such as AI release your most valuable resources, your employees, to concentrate on critical or complex tasks. There are fascinating examples, above all, in customer service. We implemented a multilingual voice chatbot for an HR outsourcing company that gave the company more time on their service offers in their call center; absence notifications and returns to the workplace were automatically managed. The HR experts could concentrate on their core activities and customer relationships.

The above examples show that new technologies, such as AI, complement and meaningfully expand a marketer’s toolbox. This doesn’t change the fact that, as before, the basis for a successful business relationship is the trust of the customer. In order not to endanger this trust, data protection and cyber security must be priorities and communicated accordingly.

Below is an example of a forward-looking company in this area: a Swiss logistics company collected a huge quantity of customer data but were concerned that the data was partly not well administered – which could lead to serious problems. A convincing data strategy was required.

Data with high strategic value

We combined our technological know-how with the client’s expertise. We bundled the data thematically then analyzed it using a maturity-level model. This way we could determine the strategic value of the data in each segment and its governance status. We found data with high strategic value and with limited governance maturity. This produced gaps we had to close with strategic measures, e.g., with a clear organizational assignment or efficient administration. The importance of these topics was soon underlined through the coming into force of DSGVO/GDPR. If a customer requested data deletion their entire customer history and all information for the company would be lost, plus proof of the data deletion would have to be demonstrated. Do you really have an overview of your customers’ data? Do you know where these are and what happens to them? Do you have access to them in order to change or delete them?

 

Can you demonstrate your actions took place? As fascinating as these new technologies are, and as fascinating are the many possibilities they open up – their deployment requires a realistic self-assessment, great expertise, and business experts and IT must work together hand in hand.

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