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MarTech - what is it really? And how does it differ from CRM, KMS & Co.? We have already answered this in Part 1 of our series titled "The next generation of CRM – may the MarTech be with you". In this article we ask the question: Which setup do I need to actually make my MarTech stack operational?
As a consultancy, we are asked this very often. Admittedly, selecting marketing technologies can make marketers quickly feel like a kid in a confectionery store: You'd love to have a little bit of everything! However, both situations are also similar in terms of outcome: An excessive appetite usually ends in a stomach ache. At the end of the day, the objective shouldn't be to obtain as many features as possible within the allocated budget, or obtain the most recent, "cool" tool, but to achieve as much added value as possible - isn't that so? But how do I "assemble" the MarTech stack which is optimal for me?
Capabilities: What do I have? What do I need?
To answer the first question very simply: If your company is online in any mode - whether via an ordinary website or a complex campaign trail - you'll definitely have some form of marketing technology in use. When it comes to the question of "what I need", we must first answer the counter-question: Where do you want to go? What is your goal? Only in this way can we define the capabilities required by our marketing stack. As an example: The requirements and equipment for someone planning a half-day bike tour to a nearby lake differ from those for someone preparing for a tour of the Alps.
To be able to define the initial situation and a realistic goal, we have therefore translated these capabilities into a maturity model
For which degree of maturity do I configure my MarTech architecture?
Most importantly, to start with: During localization within the maturity model, one aspect is crucial: Focus. Too often in projects, we find that different stakeholders set different priorities. For your MarTech stack to be successful, all departments have to agree on what is most important at and, in particular, for your enterprise!
Let's take a look at the individual categories of our maturity model using the practical example of an e-commerce company:
1. Main use case: At our exemplary company, "direct" has the largest share of traffic, followed by "organic search". The focus is therefore on user experience as well as on-site personalization. A look at the maturity model shows: This puts us in the transition from SKILLED to ADVANCED.
In our example, we will concentrate on the topic of on-site personalization. Accordingly, my click behaviour and data which our exemplary company has about me determine which articles are displayed and recommended to me. Additional incentives such as discounts or suggestions of similar products increase the chance of a successful transaction. While tactical encouragements of transactions are strongly based on the customer's click behaviour and experiences with others, strategic incentives require a detailed knowledge of the shop visitor. This is also what our exemplary company relies on.
2. Data: Which data are needed here? Because the algorithm is actually intended to trigger customer-specific results and next-best actions, a detailed knowledge of the respective customer is decisive: Has this person repeatedly ordered large quantities in the past, rarely returned items, but not ordered anything for some time now? A discount code can serve as an incentive in this case. Our exemplary company possesses data which originate from the web, app, CRM and ERP, and converge at a data warehouse. A more detailed examination reveals three further data sources in the web sector - web analysis, campaign budgets, A/B testing - which need to be merged in a data mart.
3. Tech: Only once our exemplary company is clear about the available data and - most importantly - its quality, can it start thinking about the topic of "technology". Here we take a look at three areas: Analysis, speed and consents.
- Analysis: The goal of on-site personalization is to optimize KPIs, especially those of sales. The question as to which measures work for which customer is usually too multi-layered for classic dashboards. Our exemplary company should therefore choose a tool able to cope with constant change in the data landscape: Firstly, it must evolve and be flexible, and secondly, its basic operation must be easy to learn for the organization.
- Speed: "Constant change" is the keyword. Every customer leaves behind countless data points – sometimes at extremely high speed. Loading these data into the system overnight as was the case in the past, for example, is not enough. Our exemplary company needs a fast-track tool - and must then decide whether it should transfer the data in real-time or near-time. The topic of speed is also central to analysis: How quickly do I have to establish new data, calculations or scoring? All of this affects the selection of my tools.
- Consents: Often forgotten, yet essential is the topic of "consent". In order to collect, analyze and use customer data, our exemplary company requires the customer's consent. This must be timely and transparent, similar to the fast-track process. This is the only way to prevent the usually costly breaches of data privacy. The right tool here: A universal consent management platform which provides a continuous overview of customers who have allowed or prohibited the use of their data, and relays this overview directly to the other systems.
Localization of one's own enterprise in our maturity model clearly reveals the enterprise's capabilities: What are my options, competencies and (expansion) potentials? How can my MarTech stack underpin them? What can the enterprise achieve as a result? These considerations are the basis of the selection process. Our next article will describe the do's and don'ts to be taken into account additionally here.
Do you want to set up your MarTech stack on the basis of your capabilities? Or determine your company's maturity level in a first step?
