A Practical Guide to Setting and Achieving Digital Marketing Goals Following a Plan-Do-Check-Act Approach

Abstract

Deep Dive 1 (Targets): Similar to how they are valuable in many other fields, the notion of “you can’t manage, what you can’t measure” also serves as a guiding beacon in the field of digital marketing. In this chapter the authors explain why they believe this mindset is essential for building and implementing a marketing performance management system in the context of their experience at Infineon Automotive for tracking the success of their activities along the customer journey. They describe in a detailed case study how they used a basic and simplified version of a performance measurement construct and the Plan-Do-Check-Act approach to quantify improvements and monitor progress within their transformation.

1 Introduction

Similar to how they are valuable in many other fields, the words of Austrian Management author Peter Drucker in The Frontiers of Management (Drucker, 1986) serve also as a guiding beacon in the field of digital marketing: “you can’t manage, what you can’t measure.” This mindset convoluted with us defining marketing performance management (Järvinen & Karjaluoto, 2015) in the context of the automotive semiconductor company Infineon Technologies motivated by a target model which will be described in the following chapter. To this end, the methodology of quantifying the improvements by setting up a basic digital marketing success measurement framework will be described using the plan-do-check-act framework (Fig. 1) in line with Shewhart (1939).

Fig. 1

Moving forward along PDCA cycle framework to create a success measurement framework

During the pandemic situation in the beginning of 2020, we could identify an increasing usage of offerings on our company’s webpage. Despite that, the digital marketing activities to provide the visitors relevant content with the aim leading to unique and valuable on-site experience have still been driven by people: the digital marketing team. Challenges arose, due to expectations to scale, where the call after support of automation grew louder within the company. With automation in this context, marketing automation is meant, which Bagshaw (2015) describes as the “use of software to automate marketing processes […] with far less human resource and at much lower costs.” In some committees efficiently using this kind of automation was seen as long overdue to keep and improve the position against competition. We experienced that initiating this change did not take place from one day to the other.

In the environment of semiconductor automotive industry, we experienced every day that timeframes of a quarter to half a decade are no exceptions – starting from development cycles of products, over designing-in evaluation cycles of products in customers’ projects, to long-term agreements regarding pricing. The planning cycles for digital marketing activities in contradiction where shorter, but before the target operating model was developed and applied, an annual planning cycle was the reality. Also, the development and the change to bring the digital marketing team up to speed to make at least use of the success measurement framework in a manual manner was planned for two digital marketing planning cycles.

To come back to marketing automation: Our approach was that only the right actions can be automated when it is clear what we consider to be the right actions. To make sure that the automotive division’s vision and mission are reflected in the digital marketing team’s doings, the strategy to contribute, but also make it measurable, was broken down as “reach and convert.” Therefore, the mental picture of stepping in the shoes of a potential customer, where this individual is sequentially getting more and more engaged with the company’s offerings, is depicted in Infineon’s representation of a customer journey (Blomkvist et al., 2010, Fig. 2). Conversion in the sense of being measured by the success measurement framework can be understood as identifying from the set of unknown individuals we see on our webpage the most relevant buyer personas among roles of employees of our customers (Revella, 2015) to further play on them and anticipate their interests and next steps.

Fig. 2

Infineon’s representation of a customer journey and a general mapping of digital marketing activities

To implement the strategy, the whole set of digital marketing doings we started with consists out of single external and internal firm-initiated activities (Dash et al., 2021), which one could either cluster in campaigns or other names for the work-packages, which in sum result in the execution of the strategy. The way the word campaign is understood at Infineon is as an umbrella of activities, broken down into its individual aspects: search engine advertising (SEA), mailing or social media posts, where the company pays for the execution, as well as internal activities like creating the content, which is shared via different channels. There are also activities existing, which have more an indirect or lagging influence, which is also subject to policies of organic search machines like search engine optimization (SEO). The mental picture is that each activity is contributing to one or more stages pushing the customer further in the customer journey.

We will see later, that for us giving these activities (everything that creates costs regarding money or time for the company) a digitalized structure was key to implement the success measurement framework as a model to describe how to achieve digital marketing goals. For such an instrument, also the direct connection to each responsible was seen as valuable input; since not all team members are in the management group, the execution group was represented, as well. The activities mentioned are leading to results, which are measured by the success measurement framework. Slicing the results actually is a multi-dimensional matrix. The four measuring points, which are introduced in this chapter to start describing the customer journey, are only the first dimension. The optimization methodology is universal. Drilling along the roles and activities in the digital marketing (team) is an additional dimension, as well as the company’s organizational structure and the online presence structure with the content provided there.

The digital marketing environment is an often used example to describe the difference between output, e.g., the (amount of) content published on the web, and the outcome, how the influenced person (potential customer) is reacting (Lobacher & Jacob, 2020). The activities ranked by the outcome, as preparation for the business impact, describe the reporting approach of thinking in descending sorted lists. That’s why Pareto’s basic principle was one of the main concepts for the manual approach, which we have colloquially named as “do more of the successful activities” mindset. With this approach, the perception is that the involved digital marketing team is able to quantify the individual contribution, which sums up the overall direction to move forward.

2 Plan

As indicated before, the chosen digital marketing strategy “reach and convert” is not focusing on taking a look into impressions shown on searching machines, or clicks and on-site activities on the company’s homepage. Behind every click, there is a person working for a company – a potential contact working on our customers’ projects. What do we know regarding this person? What can we do to learn more regarding this individual? And where can we identify levers to bind this one to our company? The framework we have in mind is starting from the basic economic principle of a maximal principle. We only have limited resources regarding budget and especially time of the executing specialists, so there is a need to allocate this budget in a way that it optimizes the result (Eichhorn & Merk, 2015). How we measure the result and describe the possible activities within the budget will be described in more detail later.

2.1 Target Operating Model

Furthermore, the approach with a target operating model by setting ambitious targets and knowing that without a global success measurement framework as single source of truth in place, the targets won’t be achieved, was the team’s motivational basis to create a sense of urgency for focusing to get better and reach the targets. In terms of measuring, describing, executing, and integrating digital marketing activities, we interpreted “better” in this case to mean “different” from how it was previously done.

An often-experienced expectation, on management level, as well as on working level, is to find an easy connection from digital marketing activities, or even on-site activities, to materialized revenue. Having this as end in mind helped us to benchmark the department’s contribution to the corporate strategy. But what we experienced also from data-driven point of view, why various groups within the company have failed, when they wanted to connect digital touchpoints with turnover, was that measuring points being too far away from each other were tried to be connected and the various paths from a to b consisted out of too many unforeseen detours like customer granularities. So, starting from the digitalize-able end, namely, customer projects, and identifying possibly some success stories (the one business out of one thousand, which we can link to digital activities in maximum half-digitalized ecosystem), we have had in the back of our mind. But the lever is in this chapter on measuring the digital activities and describing the discoveries along the funnel of materialization: In the automotive semiconductor industry, starting point for the digital marketing team was slicing the big picture from awareness to qualified contacts, who the respective responsible sales community can further work on and which then later can materialize into business opportunities (customer project pipeline digitalized), which might in the latter lead to quotes, orders, and finally revenue.

What is also meant with the big picture is taking a step back and viewing the overall result from bird’s eye view: the sum of all results which we see. And when we drill down, which share are we further evaluating? We esteemed this feedback loop to not get lost in evaluating details, which did not have a lever. Moreover, while developing the model we described success in terms of: to become confident, that we can say, we are on the right track.

With the word result, we mean the pure numbers in terms of fulfillment, independent on what they were triggered by. The result view was seen as worthless, in case the team was not able to describe, what triggered the results: What was executed? How can we then improve with a best-in-class mindset? We could sharpen the understanding that even a structured set of slides is not sufficient to describe what activities are performed and how they are to be described. Digitalization in the field of describing the activities in a digital tabular way was identified as key to apply the target operating model, even though it was a challenge, because it meant change not only in the digital marketing team’s behavior. Such a transition was not seen as only to be done by assisting functions too far away from the business context like information technology (Hunsberger, 2017).

With what was described so far, the only need was to have a manager in place, who can map basic economic principles in a performance management environment. The manager afterwards decided that on the digital marketing context content-wise experts were required to bring the activities and the expected outcomes together. And in the very end, the team was enriched by data experts. The content-wise expert team’s understanding was that not each activity will perform strong on every outcome. Digital marketing experts brought in their expertise, which types of activities are existing and what the theory says that they are good at, seen from their academic background. The approach was to bring experts from campaign management and execution, from marketing communications regarding content to publish on the website, as well as website experts together. This was seen to ensure that the relevant involved parties along the digital marketing processes in a stock listed company gave their inputs and it fitted to the possibilities a digital marketing team – who are no experts on the complex products – had.

The company’s central sales and marketing supporting function for various business models communicated that a generic digital success measurement framework adds value. The number of possible touchpoints along the customer journey was broken down by the described committee of content-wise experts for the automotive section within the company, since the automotive business in the company is perceived with particularly long design-in cycles and long-lasting deep customer relationships (Burkacky et al., 2022). As stated, there was practical confidence on the one hand side that the relevant touchpoints are measured, but on the other hand side that the overview was not lost, such that only a hand full of KPIs were considered.

To retrieve, the basis was the customer journey from awareness to sales. The following describes the simplification along this framework, how the expert committee broke it down. Within the marketing aspect of leveraging the number of contacts to further play on, we defined the starting point as the amount of suspects we see on-site (KPI 1), who open up the funnel. The pure number of suspects, who are leaving their touchpoints on-site, is relatively high. But we know much more about the subset, which is quantified by the number of newly registered contacts in our contact data base (KPI 3). Last but not least, the number of qualified contacts was seen as relevant to describe the development of the amount of these persons to potentially follow up on (KPI 4). To enhance the knowledge regarding these individuals, it was seen valuable to describe their interactions, on suspect level, via the number of relevant on-side actions (due to a good mapping of content and their relevance for the business – KPI 2).

The relevant target group of digital marketing was defined as focusing on engineers, who check technical specifications, which can be derived from technical documents. For the registered contacts, we directly have the information, which activities have triggered certain behavior, such that we have way more clarity on this smaller set of persons. Although it would have been possible to explore various other touchpoints in more detail, we decided to focus on the ones mentioned as a starting point within the existing reporting ecosystem at that time. This was done to avoid losing sight of the personas and to maintain an overall perspective.

Regarding meaningful and precise definitions, the team was able to align that it was not required to be on the same detail level how definitions are required, e.g., in the academic probability theory. But less interpretation given by the communication of the KPI definition supported the discussions with the stakeholders. No time was lost in bringing the whole organization to the same understanding of the numbers and their statements. To reflect the levers along the overarching “reach and convert” strategy (Fig. 3), we aligned on the definitions for the four already mentioned KPIs in Fig. 4 as a starting point.

Fig. 3

Digital strategy “reach and convert” with the measures mapped in a funnel

Fig. 4

Starting set of digital marketing KPIs mapped to Infineon’s representation of a customer journey. KPI1 – suspects: Unique users and yet unknown individuals traceable/measurable through any kind of on-site activities on the company’s website. KPI2 – on-site actions: Number of on-site activities by suspects on the company’s website. KPI3 – new contacts from our (potential) customer base: Unique known contacts added to the contact database, identified by their e-mail address, which we are allowed to contact after they have given their consent in accordance to currently valid GDPR. KPI4 – new qualified contacts: Known and “sales-ready” contacts in the contact database, who have indicated a specific interest in our offerings based on a defined lead scoring system

With the Automotive division being one part of the entire company and with the existence of central sales and marketing supporting functions, those definitions have been aligned with the central process owners to guarantee consistency top down. Three out of four measures are counting individuals, which are so-called “semi-additive” measures. This means, e.g., one suspect triggered via several digital marketing activities still remains only one person, that we reached. So, we needed to be very careful, when evaluating the result of our activities, to not overestimate the amount of persons, who are interested in our content, by aggregating numbers since their values cannot be directly summed (Bhaskara et al., 2018). That’s why we decided when drilling down, the aggregated numbers become invisible because the semi-additive measure cannot be simply summed up across all slicers (Fig. 5).

Fig. 5

Example of the semi-additive measure suspects reached by two campaigns with intersection of the suspects

Despite the technical challenges, the slicing and dicing was seen as the value-add, starting from the big picture and drill down into the details, which can be mapped to individual activities. Clustering the activities and restricting the number of groups to maximum ten and clustering the rest in an “other bucket” was valued by the team as key for the usability of visualizations. Here, we also made the experience that the stakeholder management is important to benchmark the theoretical digital marketing clusters with the importance of certain activities perceived in the marketing community. Considering the case that the data quality is never perfect, we identified that this is an area to get the buy-in by describing the past pragmatically by using lookups. There was some data available, but the required fields were not (yet) filled, due to lack of sense of urgency or a process. Some connections needed to be maintained for a longer time via lookups, until the organization moved. This might be seen as hard manual work, but keeping the end in mind triggered by the target operating model, this was seen as a suitable way to get first insights and get early into the actionable mode. To link further information beyond the customer projects the digitalization of customer interactions supported by sales was required, but elaborating on this is out of scope for this chapter.

2.2 Approximations and Initial Target Setting

What we have described so far was moving away from presentation slides. The buy-in was supported by giving the stakeholders some dashboards, which they can click through. Even though the management intelligence was seen in the economic principle convoluted with the performance management mindset, the described approach was supported by repeating the model on and on, as well as the concrete visualizations, numbers, and actions derived are shared. But how did we come up with a first version of ambitious but realistic targets?

Measured touchpoints and evaluating historic numbers on top level we mentioned. The thought must be allowed, that without performance management in the past, every increase in the numbers must have been either “organic” or depicted the market interest, or was driven by external factors or distractors. When acquiring a competitor and integrating their pages, of course the numbers measuring suspects and on-site activities are increasing. But to remain then on the same level of growth, one would need to acquire every year such a company, which might not sound realistic. So, what we did was taking these external influences out of the long-term rate of increase. The approach was that with setting the focus on “doing more of the successful activities” a target higher than the organic growth can be reached, where the cleansed historic trend defines a first lower boundary.

We have talked about the lower boundary, but how about a realistic upper boundary? Industry standards were taken into consideration. Knowing that in certain industries the absolute amount of contacts might be restricted, especially the amount of valuable contacts was not expected by us to be infinite. The calculation approach can be described with using conversions: three out of four KPIs mentioned are a number of people, where estimated conversions from the industry were used as a feasible indicator, what could be possible. Benchmarking the real numbers against industry standard numbers led to vast deviations in some areas. Of course, a lot of processes along, e.g., the contact qualification or hurdles in the registration process, were the reason for the numbers, which are not showing the expected result. This exercise also led to depicting these situations, but going into more detail is not in scope here.

Nevertheless, the initial targets described were set, due to the boundary conditions, in the middle of the upper and lower boundary. Furthermore, hard numbers have been identified as the more stable increase factors compared to percentage increases. Avoiding percentage increases year over year we decided to use to ensure the understanding that long-term targeting is not modeled as exponential growth with even increasing percentage factors.

We experienced that external factors can make a target a vulnerable instrument, especially, when targets are linked to annual personal targets. It turned out that an open discussion culture (e.g., in team jour-fixes) helped to get the buy-in, since the entire digital marketing organization is dealing with the topic that this change along the motivation triggered by the target operating model needs to be performed. The level of the sum of all campaign-related activities in practices turned out to be a feasible level to check the match of top-down and bottom-up targets, because here each specialist was able to relate to the activities in the own area of responsibility, and yet had the chance to improve the mix of activities for the next planning period.

2.3 From Reporting to Analytics

What has been described so far was an approach for a performance management environment supported with advanced reporting functionalities. But the way to apply the often-used word of analytics is not trivial. We have talked about the performance management mindset and this was key for the entire methodology: This supported us in defining the decision variables to optimize on our KPIs. The lever we have are the activities, so the sentence of “do more of the successful activities” became central. Digitalization was key to describe what happened in terms of digital marketing activities. To the activities, we added the results measured via KPI 1–4 (and, e.g., weighted by the budget used), and we got for the start a four-dimensional model to optimize the decision for successful activities on. This gave us the what (was successful).

But what made these activities successful? The end in mind was, while performing so many activities, which a single person cannot know each of them in detail, that an intelligence supports us in predicting and proposing, how to design the activities and on which activities to focus. Therefore, we needed to be able to learn from the past, in the following subsequent way: describe the activities with as many relevant columns as possible: up to a certain degree, the more information digitalized, which give us insights, how different activities are designed, the more variables an (artificial) intelligence can use to build a model on (Frost, 2013). The criteria to characterize the successful activities were the columns of the second table, which described the activities content wise. This gave us the why (was it successful).

What has been described is deriving insights from quantitative criteria. In addition, we considered it advantageous to leverage knowledge about future trends that may not be evident in the described model. To achieve this, we proposed a collaboration between engineers, who have direct contact with some of the targeted personas, and digital marketing experts, who have a comprehensive view of the digital marketing results and can identify an aligned go-to-market approach. Sharing the digital marketing views already during the development with a larger audience took take time because of the questions, which appeared, but we were able win the technical marketers. Furthermore, we experienced that stakeholders in the organizations pushed into the direction, on which they are incentivized, but what we describe here is the lever of digital marketing activities along the measures the entire automotive division’s management was confident with and have been broken down by digital marketing experts.

We stated that as a starting point the currency “valuable contacts” was defined. When joining customer projects’ opportunity value or revenue figures, which are not fitting, then the described optimization can lead to diverging results, which might not be optimal in the sense of the digital marketing strategy, measured by the four digital KPIs. But we agreed that a model is nothing, which was stable forever. So, during the cycles, we evolved during a steep learning curve to match those numbers and then improve the optimization model with adding a fifth dimension, since only parameters needed to be recalculated, but the methodology remained the same.

3 Do

After sharing the initial framework and the mindset, we emphasize that we think it is crucial to get the hands dirty and spend the time digging into the numbers and crawling through the data.

3.1 Setup

We thought outsourcing might enable certain other levers. But having significant data insights in the digital marketing team itself supported the approach described. Additionally, this was seen as value-add to get the buy-in from the stakeholders, when questions can be answered by persons with in-detail understanding directly, without stating that we have to get further information and will come back later.

One sentence, which might not like to be heard: data engineering and data cleansing were not the most attractive job. Nevertheless, it needed to be done. Neither could our team perform analytics nor derive information, on which decision can be based on without meaningful, connected data. However, our team did marketing. Even if the data was not perfect (and most likely never will be perfect), implementing methodologies and learning about the approach was something we did not wait for. Especially when the data then was available and not enough time would have been spent to evaluate methodologies, then the result would have been poor.

During the conceptualization phase, the lever became clearer and clearer that the key decision criterion was doing more of the successful activities. And therefore, understanding results, which were derived from the evaluations, needed to be readable. We decided for descending sorted lists, sliced by the digital marketing activities, then sorted by the result measured with the KPIs. This was directly shared and benchmarked with the cross-functional stakeholders from business lines, sales, and also the marketing management. Every employee, who has worked in a central reporting (supporting) function, knows the challenge with reported numbers. There needed to be the one number to be in place, which is the truth within the whole organization. Otherwise there is too much time and energy lost in explaining the number from the own organization’s point of view. That’s why alignment with the central functions was seen as very important, even though they might have their own agenda. The central function’s number was always taken as given after they were aligned and concepts like the semi-additive measures learned by heart, but we were able to slice it down along our expertise and the activities, which we knew in more detail.

We already elaborated on the KPIs. Once we understood the mathematical challenges involved in counting possible contacts with the measures, we were faced with content-related challenges in determining whether we were truly optimizing ourselves on the right touchpoints. One challenge we experienced was the differentiation between registrations and new contacts. After finding out in exchanges with the IT department that a suspect can register more than one time, we had to clarify that we are optimizing along new contacts and the focus is on activities, which lead to real new contacts in our contact data base, and not the amount of registrations button clicks. Because checking the numbers in detail showed us that the amount of registrations and the amount of new contacts behaved differently and thus might have led to totally different conclusions. What also enabled a strong buy-in, especially in the digital marketing team itself, was describing the qualification of contacts in our contact data base by the stock and flows model after John Sterman’s Business Dynamics (Sterman, 2000): One person at a time can always be only in one status (one bucket). But over the time, one person can be qualified and flows from one bucket to the other one (Fig. 6). Our contact-KPIs were measuring the conversions along those flows.

Fig. 6

Contacts stock and flow through lead qualification based on Sterman (2000)

Another personal requirement to implement what was described was having team members in place, who speak two languages, namely, the business and the IT language. Other departments experienced that without such a translator in place, too much energy and time was wasted, when requirements could not be described properly and just a huge set of requirements was handed over, which was not needed in the end and overloaded the IT department.

We also made the experience that learning along our journey of rolling out the information was not seen as a blocking point. We started with the best that was at the time available. The methodology was seen complicated enough to translate the available information in views along our activities. With more and more data, the results converged to a few statements.

3.2 Clear Data

Thinking from the end in mind was also seen as key for the beginning of the data engineering part. From the very beginning, we focused on having meaningful numbers in place in terms of magnitude. Therefore, sharing the logics with the direct stakeholders, as well as touring through the company’s marketing analytics communities supported the minimum four eyes principle for each step of the data blending. What we started with and shared with the organized, after we made sure that no inflated or deflated results are provided, we called “dashboards.” But actually, these were small databases within themselves. On the one hand side the complexity in the calculations to reflect the measures supporting the digital strategy did not allow flat lists which can be exported to spreadsheets, and on the other hand side the views provided were required to be broken down for further evaluation to a certain level. The levels we broke down are explained below.

There is an organization existing, and as long as the company is organized and incentivized along a business line structure, we decided that this needs to be reflected in the evaluations, as well. Even though there were and are still challenges to compare two totally different business models, the internal comparison facilitated discussions about digital marketing approaches. This supported the buy-in to spend efforts in digital marketing within the line organizations, because the managers were able to ask, what the other managers were doing different or were setting digital marketing focus, at all.

Additionally, the digital marketing activities are taking place in most of the cases on the company’s website. So, identifying the success on different areas of our homepage was seen as key, especially the product structure communicated to the outside world. The combination of the two mentioned dimensions organization hierarchy and homepage hierarchy initially was solved with lookups before implementing the success measurement framework. With the pure number of products, this was identified as one of the few areas, where lookups were not the way to go, when they are not maintained centrally and automatically.

Furthermore, we identified levers and regional specifics are important to be identified in matrix-organization, especially when there are counterparts in the regions, who have a significant autonomy. “What can we learn from regional activities?” and “is in other parts of the world the language a criterion?” appeared as discussed questions and found their way into the evaluations. Filtering was also required along the digital marketing activities, specially to focus in the stakeholder management on the activities for a certain campaign. The activity view will be described in the following paragraph in more detail.

As previously mentioned, campaigns are composed of a collection of activities. Initially structuring campaigns in detail assisted the entire team in comprehending the individual components, such as activity groups, timeframes, and affected webpages. Moreover, breaking campaigns down by their financial implications in terms of expenditures facilitated an additional use case. In a second step, incorporating the KPI results from the previous year allowed for the weighting of individual activities. Now in a third step, the best-in-class activity quantified by the maximal KPI value result per activity group (e.g., from all social media posts) could be identified to know which activities and activity groups have performed best. Using the most successful activities from data point of view also provided us a basis for detailed campaign target setting, which paid into the overall target fulfillment along the target operating model.

We related to the fact that long enough timeframes are required to determine the long-term trend to answer the questions, whether we are on the right track. To be able to fully decompose an economical time-series properly, (Kleijn & van Dijk, 2006) state that four cycles are required for a proper description of the dynamics. However, using on the long-term linear trend worked out as a good starting point, especially, since the journey to implement, roll out, and apply performance management was a more year cycle, in the meantime, the data was collected. Beside business cycles, in the communication we made our stakeholders aware that sometimes a lag appears until an activity unfolds its full potential; SEO was the most often communicated example.

3.3 Connect Data

We had on our mind that the strategy was called “reach and convert” – into possibly interesting contacts. So, making sure that all the measures were in sync meant: reduce the sources to make sure you do not get biased by double counting persons to overestimate certain influences (e.g., one person first on a third party’s webpage and later on our own webpage still remains one person to convert into a contact).

For most of the required data, there were already all master data drill downs available in the corporate data warehouse. For the general buy-in and the automation, it helped a lot to use those tables. Therefore, exchanges with employees, who have worked in central reporting of revenue or other figures or in finance before, were seen as a value add. For customer master data, product master data, and even calendared master data in the corporate data warehouse, we did not need to reinvent the wheel. It was even seen as dangerous to have own lookups in place, since organizations changed, especially with the fiscal year changes.

In the digital marketing team in the automotive division, we felt like being caught between the chairs. On the one hand side, departments like IT or central marketing strived for fully end-to-end implemented data pipelines and processes with a first-time-right approach, and we felt the business lines were too far away from the technical and legal challenges. What was described above can be broken down data wise that we are tracking two types of information: the anonymous web activities and the personalized contact information. The full and automated connection of these data sources was a corporate project and was not solved within a division’s digital marketing department within 1 year. Taking the audience from the business lines with us on this journey along user stories and pilots was seen as valuable. Furthermore, this also created awareness that the connection with other data points in preparation for a customer data platform was only successful, when the entire organization moved and spent the effort. Therefore, management and their executive assistants were involved right from the beginning. But even in the initial environment with a separated web and contact reporting, one practical challenge our team faced was sticking to the campaign name coding on the activity side and assigning the campaign codes in the results to the result side. To align such coding on result side, a clear understanding that a holistic aggregation is required helped to move different supporting functions to use the same campaign coding, which then helped us a lot.

3.4 Interpret Data

One question that appeared was regarding the explicit users of such views described. The plan was that every team member and executor of digital marketing activities should use it along their use cases. However, we realized, it was the team leads and above who used it first. It was seen as crucial that the overall approach was understood by the operative team members, too, and that questions on benchmarking were allowed. Nevertheless, we experienced that the management support helped to bring the data-driven mindset into the digital marketing organization.

For example, in the business line organizations, who are focusing more and more on digital measures, the question was allowed: Which levers were existing for them? Checking a development of KPIs with clear actionable possibilities to improve the situation was identified as useful, since it supported the roles and responsibilities introduced with the digital marketing departments. The digital marketing teams have been able to identify distorted information, to the benefit or the disadvantage of the organization, which have been communicated and subtracted out. Even with not 100% accurate data, the right group of questions appeared with the insights provided.

A totally different, but possible also worth knowing aspect was the comparison to the recent previous business cycle. An additional value add was seen in the quantifiable amount of contacts reached in the near past and possibly to be reached, when taking a look outside the company and not restricting oneself to internal data only. The success measurements framework’s aim was not to model the customers’ employee fluctuation but to describe the current state in the market, and providing the sales community insights, which they have not known before and not already digitalized in the customer project funnel.

4 Check

It was seen as helpful to understand the moment, when it made sense to start verifying that the approach described leads to the first results. As stated, we interpreted “better” as “different,” and when we experienced that there were really the activities done differently, the whole organization understood the approach of doing more of the successful activities beard fruits. Otherwise, in the past external factors must have been the reason for the different results. Of course, also we experienced that during the manual process of setting the attributes, which described, e.g., the groups of campaign activities, some touchpoints were not set with the correct values or have even been forgotten to be set. This led to a shorter timeframe, where numbers could be evaluated. In some cases, further one-time lookups in the past could be done for corrections, but when a touchpoint was just not set, then there was no chance to link the results.

Again, different results were derived from different activities: In the automotive semiconductor industry, there is a lot of planning done for timeframes, which last several years (Burkacky et al., 2022). Even though the digital marketing activities are set up in shorter cycles, those cycles have also been annual cycles in the past. Planning took time and processes with several stakeholders were established over the entire business division. Regarding the timing challenge depicted, better results based on the approach described could only be expected, when the (annual) planning has been adjusted after learnings of doing more of the successful activities. Regular performance reviews with the digital marketing experts have been established, where the digital marketing approach was not stopping by showing a red, yellow, or green traffic light in terms of target fulfillment, but also pointing to the best-in-class activities to learn from them.

When the success measurement framework was first shared, the time horizon was seen as a foot note. But the 3 years mentioned to establish the success measurement framework and bring the system up to speed became more and more realistic. Even though processes and clear roles and responsibilities have been implemented before, the target operating model approach was shared for the first time. The same behavior was experienced, when taking a first look into the results after the first year. They only showed what we called “organic” growth. Why? We did not do more of the successful activities, because when planning the first year’s activities, the team has not been aware, which have been the successful and best-in-class activities. This could only be derived with the lag or agile interventions. So, the understanding, where one is in the cycle, was seen as crucial. An agile mindset might have positively influenced this area, as well (Hunsberger, 2017). We experienced that performance reviews during the planning cycle enabled corrections up to a certain degree during the year.

To mention an additional challenge, which we faced, was that the enablers’ systems like search engines also led to additional lags. To unleash the full potential of digital marketing activities, a time-dependent tracking until the big search machines’ algorithms started to turn into results, in some areas led to an additional delay of some quarters (Fig. 7).

Fig. 7

Implementation of success measurement framework, performance management cycles

5 Act

Once the first success stories could be provided, the entire organization could make use of the success measurement framework, and we valued that the business lines spent the time to follow us on this journey. It was totally clear that there are the leading departments, as well as the departments, for whom it was more challenging to change their approach or even did not have the digital marketing agenda on top of their minds. We also experienced: Do not forget the stakeholders or team members, which you think that they are informed anyhow, because they are relatively close – but not close enough. For example, the entire digital marketing team, who were identified as important multiplicators, but were not in the project’s core team. One of the biggest levers we identified in the digital marketing team itself was moving oneself in the mode of using the information derived from success measurement framework and by that restricting other sources from which we knew that they were insufficient. As described, it was very useful to have the user in mind while conceptualizing the views, but the team needed to use it, as well. Therefore, triggers were set by providing centrally on regular basis a data-driven evaluation for the digital marketing functions. Those evaluations’ read out in team jour fixes created certain awareness and helped to solidity the approach, because the hypotheses, which buzzed around verbally, then were written down. In follow-up tests, these hypotheses could be refuted or supported within the own tooling and only few additional statistical efforts.

For the success of the success measurement framework, it was very helpful to be very close to the dedicated use cases, which the business line organizations knew and had understood before. During the development of the success measurement framework, we identified that a huge set of users have worked with views in the past, which have used only slightly different measures. So, we guided them from the approach they had used in the past to the views we had created specifically along the digital strategy. The basic data remained unchanged, but the same information sliced by additional attributes were provided. Especially bringing the concept of suspects being a semi-additive measure into the organization opened their eyes, which large deviations have been overlooked without the success measurement framework in place. So, we could convince them that using only one measure pruned the amount of information, which would have led to less focus otherwise. We called this the “fighting guide” to use, whenever someone came around the corner with evaluations based on impression or was asking for reporting of pageviews because when for one activity the suspects measure indicated good results and another measure for awareness bad results or vice versa, we overcame the situation by focusing on suspects and knew which suggestions to present.

Moreover, with the state-of-the-art technical reporting front-end environment provided by our IT department, we started to send out a standardized reporting via e-mail on regular basis to penetrate the own organization, especially to have the big picture of the own area of responsibility on regular basis.

6 Summary and Outlook

The essence of this chapter lies in the methodology of modeling. We pointed out that the model was and is a living model. As this journey evolved, the touchpoints coming closer to the center of the company’s business model have been taken into consideration: additional KPIs can be connected, their results to the activities can be added, and it can be optimized on then six and so on measuring points. The methodology remains the same, and the model is just getting more precise with time and of course leads to new insights and thus different suggestions and decisions taken by the digital marketing managers.

Digital marketing at Infineon is seen as targeting a market with dedicated go-to-marketing categories. On the market there are business opportunities, with individuals being active and working on those customer projects. Behind every individual our understanding is that there is an individual customer journey, so opportunities and individuals are seen as to be crossed from result’s perspective. The concept of an attribution model, which credits the individual activities driving the contact through the journey (Shao & Li, 2011), can be applied and the respective logics fed with the data used in the success measurement framework to take the next step identifying best-in-class activities, e.g., in the way (Dalessandro et al., 2012) estimate activity performance.

Furthermore, we are working on predictions for business opportunities, which are identified, clustered, and worth to be pursued by the salesforce and where it was in the current environment not able to identify them with the existing approach. Then the success measurement framework’s successor is not only supporting to achieve digital marketing goals, but also giving our stakeholders in the sales area an information they did not know before.