Business Intelligence

How to know your customer with Marketing Automation? Effective practices - an interview with Lukasz Nienartowicz

Pictured is Lukasz Nienartowicz Head of Business Intelligence at Britenet.

Lukasz Nienartowicz - Head of Business Intelligence at Britenet - in an interview with Michal Kokoszkiewicz of the Commercial News portal, talks about, among other things, challenges, solutions, tools and implementations related to marketing automation.

How do you and Britenet define marketing automation?

Marketing automation is a set of certain business processes and tools that involve three things. First, if we are marketing to a target customer, we are usually able to execute a dozen or so, maybe a few dozen campaigns a year at most. With automated marketing, we're talking about a hundred or even a thousand campaigns at a time. 

On the other hand, the purpose of this marketing is to communicate very precisely with the customer. The number of campaigns or target groups we create means that we can personalize this marketing. It can be tailored to the smallest possible group, that is, even one person. This is practically unattainable, because we are talking about the preferences of a specific customer. The main thing is to make such a group as precise as possible. 

The third thing is that the whole environment must ensure consistency of the message. A person cannot be bombarded with information in different channels of communication. If we are communicating through several channels, there must be one consistent message in each of them and one place from which these messages can be managed. If a given system is able to meet these requirements, it is one that can be called automated marketing. 

What are the problems with marketing automation in business, especially in commerce? What does the market really need when it comes to marketing automation?

To do marketing in an automated way, first of all we need a tool. The next elements are our customers' data and the business environment. Speaking of data in the retail or FMCG industry, it should be noted that organizations of this type do not have a long tradition of collecting personal customer data. For a long time it was not in their interest. The company mainly paid attention to the product itself and promotions. It did not know who the customer really was. E-commerce was already active in this area from the beginning, while classic sales channels started to join much later. Long-established organizations have a problem with scattered data, and no one initially thought about data quality - new systems were added, and data was duplicated. By dealing with data from the beginning, it can be taken care of more in a way that puts the customer at the center. On the other hand, in this type of organization, customer data coverage is not always very high, which creates an industry-specific challenge. Organizations have created their loyalty cards or applications over time. They are starting to collect information or have been doing so for a long time. They are able to name their customer, notice what they have bought before and what they were interested in. Collecting data and naming the customer is essential if you want to talk about a precise message to a single customer. 

Another piece of the puzzle is the tool itself. Here there is a problem with finding the right one, because today we have an overabundance of all sorts of IT systems that seemingly pretend to be used for marketing automation. It may turn out that there is a little more advanced mass email "sender" behind it, and we are not doing advanced marketing, but attacking our customers with more messages. This doesn't lead to anything good, and is downright dangerous. We need to be aware that we are looking for a tool that is quite advanced if we want to automate marketing, because it will handle different channels at once. Contact channels can be apps, phone messages, social media, emails or classic letters, so you need to know how to handle them properly. The search for such a tool is an important element, but we also need to find someone who can understand it as well as apply it. In this case, we need a strong team or a trusted partner who is able to help us.

The third element is a business issue - it's about preparing the organization. If the number of campaigns has increased, then we need to change the company in such a way that it is ready for the new approach. These are the three areas where the most challenges arise. 

‍What does themarket offer in terms of tools, schemes, marketing automation systems? What is currently trending?

There are too many tools to talk about them in detail now. Watching the market for some time, I was a little doubtful that such a good tool would be created. Even with my team we were considering creating one. There were classic tools from big vendors like SAS or IBM. They had great capabilities, while they were very expensive and not based on a cloud solution. Nowadays, the cloud standard is more and more common. You had to have a really good reason to make such a solution worthwhile. Prices usually depended on the number of customers, and it didn't have to be financially worthwhile at all for mass sales. On the other hand, what started to happen in the cloud was very good, while it did not give the whole spread of what such a tool can do. 

About three years ago, I asked my colleagues on the Salesforce team to check out such a tool, and it turned out that it was not fully ready. Over the course of several years, Salesforce bought out several companies and implemented their solutions into its own. Only after bringing them together did it get a full-fledged marketing management environment, which took a long time. This is not easy, as it is a collection of several functionalities that must be owned and used - this must be provided in order for the tool to fulfill its tasks. Unfortunately, there are many incomplete systems on the market, but better ones are emerging. Some companies tie up with a market leader and buy a tool, then start looking around for someone who can implement it. A problem arises because the money has already been spent, and there is no person who will know how to do the implementation.

‍Whatshould a company know before implementation? What aspects should it research or prepare to do it right?

The key to a good implementation is a business issue in the first place. Some people think that implementation is about installing applications, and it absolutely does not work that way. To implement a system, we need to look at our organization and answer the question: are we ready for it? We need either on our side, or on our partner's side, people who know how to carry out this business process. We need to be aware that for more campaigns, we need a process where we know: who handles what, how it's going to go, who decides that this campaign starts or ends, who monitors it. This is a process that needs to be built, which is lacking in many organizations. 

Next, we need to have a plan that the campaigns will follow. These can be regular or dependent on the customer life cycle (customer life cycle). The consumer has a specific buying cycle according to which he appears in the store, and we should know what stage of this cycle he is currently in. Considering multiple campaigns in one plan, we need some kind of description template. With it, we are able to see which campaigns we are dealing with and to whom they belonged. What is key is the rule of contact rules - we can't attack customers with a large number of messages and notifications from one campaign. A customer who is in the base of a company about to launch a thousand campaigns is likely to be included in many of them. His patience may run out when he receives a very large number of notifications. The situation is not ridiculous, because such cases have really happened. It is the customer who decides whether to accept marketing consent, and by default he often does, but if we overdo it, he may very quickly give it up. These situations happened because proper contact rules were missing. This is very important when deciding on several campaigns, for the reason that we are risking the asset of valuable marketing consents. 

We also need to have an idea for such a tool so that those who will implement it know what to do. Even the specialists themselves can ask about our plans for the tool. If we don't assume what we want to do in this regard, we will not be successful. Unfortunately, this happens repeatedly, as we implement a tool and only later think about its purpose. 

‍What doesit look like on the cost side when it comes to marketing automation? Are these types of activities a major financial challenge for organizations?

Any class of system of this type will be a challenge whether building a CRM or a marketing automation system. With cloud solutions, a huge plus is that you can start gently - you don't have to include all your customers right away. We can try to do a few campaigns and see if they work by adding more target groups. There is an element of learning going on here, which is a good thing for people to learn about a tool. The funny thing about changing an IT system is that the hardest thing to change is the users themselves, who have to adjust to the new way of doing things. This needs to be planned in advance and you don't need to spend a lot of money. It can be done very nimbly - without too much stress and in the right order. 

‍Canyou perhaps give some examples of marketing automation in business, in commerce, that you personally find interesting, that you find successful, that you find profitable for the organization?

These systems are built so that the customer does not notice that they have been implemented. A situation in which the consumer felt the effect of such a marketing automation system, receiving much more information, is not a good signal for us. He should get more tailored content, instead of more content. There are such implementations in Poland and abroad that are worth mentioning. Let's answer the question: where did such systems come from? They worked before in such organizations that recorded a long customer life cycle. Such an example is the sale of a car - here the life cycle of a consumer is long, for the reason that a long time must pass before such a customer buys a car again or comes for service. We need to build a whole customer life cycle, which will include many different possible events. In a situation where we assume that someone replaces a car every three or four years, we need to manage this time for proper communication. At some point, we need to anticipate the customer's return to the market and their desire to buy another car. Such implementations, from which we can learn, are operating in the automotive industry even in Poland. 

If we look at retail, there is a slightly different problem here, because usually the customer has a shorter life cycle between events when he will want to buy from us or from a competitor. In such situations, solutions in which we take care to suggest purchases to him are more useful. One Portuguese company boasts an interesting implementation, where we saw a message from a customer thanking us for reminding him to buy a product. This is one approach that is profiled well enough that we know what the customer is buying and will buy. This is something that differentiates retail from industries where we have had the marketing automation theme present for a long time, because this customer has been named for a long time. 

We can also learn such solutions from other industries - they are present in many banks. In one such establishment, it was quickly learned that one should not exaggerate communication with the customer in real time. There were situations in which money came into a customer's account, which was a signal to offer a deposit. The person who held the account was not yet aware of receiving the cash, and had already received a message offering a deposit. The pusillanimity could have been frightening to users. This example teaches not to overdo it with this type of solution, as the consumer may feel too much scrutiny. It's important to strike a balance, to be adaptable, and to be calm and detached in your actions - this is something you need to learn. 

Banks already know that the user must be given a moment and cannot react spontaneously. In the context of a customer's short buying cycles, one should not overdo it either. A person who visits our store once a week should not be offered deals the next day. The insurance industry is a sector that we can also be inspired by, keeping in mind the life cycle of our customer - I mean selling more insurance instead of one. The customer life cycle and understanding it is one of the basics to be able to freely implement the right tool. 

‍Canyou outline what will happen in terms of marketing automation in the future?

As for the future, we need to look towards cloud solutions. Looking at the Western market, we can see that there the tools must be in the cloud. In our country, still many businesses are afraid to move data to the cloud, and we should get used to this solution. Looking ahead, it is important to pay attention to regulations, for the reason that RODO, which entered a few years ago, has managed to change the industry. New marketing consent laws are popping up all the time. Some of these systems operate on the principle of analyzing Internet cookies - in the future, they probably won't. With these changes, you will have to take great care of your business, which will work in favor of marketing automation. 

Taking care of proper communication is key, as customer awareness is increasing every day. Machine learning (machine learning ) is important if we want to build the right customer experience. In this case, we are forced to use advanced analytics. It's about greater personalization of ads, promotions and looking for what we can still offer the consumer. There is also the issue of studying the preferred channels of communication. In the case of a multi-channel system, we can set up multi-stage campaigns across multiple channels. We can also begin to study whether a particular customer prefers a particular channel and his or her reactions to the messages. Sending information through channels that don't interest him will prove pointless. There are also more complex issues, such as predicting whether a customer will leave us for a competitor. These types of operations appear in advanced analytics. It has the advantage of not yet being so developed in systems. Modules for segmentation or analytics are built separately, and when they are ready they are added to self-marketing solutions. In the future, we can expect systems to have increasingly powerful modules from machine learning, so they will be able to do more when taken out of the finished package.

You can also find the conversation between Lukasz Nienartowicz and Michal Kokoszkiewicz of Commercial News HERE.

Profile photo of Lukasz Nienartowicz

Lukasz Nienartowicz

Responsible for the development of the Business Intelligence area at Britenet. For more than 12 years he has been involved in building data warehouses and analytical solutions for industries such as banking, insurance, automotive and public sector. He is particularly fascinated by the area of client data processing and analytics. He specializes in advising clients on how to overcome their business challenges and grow their organizations with the help of data.