Marketing automation without strategy
As Stephen Covey said in his best-selling book, "Begin with the end in mind." We should first be clear about what we want to achieve, what the end result should look like. For example, it makes a difference if we only want to automate email marketing, or if we also want to dynamically personalize the content of our website or e-commerce application. We also need to consider in which sales phase we want to automate. For example, is it about shortening the sales cycle in new customer acquisition, or do we want to expand customer retention by means of loyalty programs?
Once we are clear about the "what", or the desired outcomes, we should think about the "how", develop a strategy and specify goals. To do this, we need to define a few metrics: Key Results (OKR model) or Key Performance Indicators (KPI), and also of those that are not only relevant for the marketing department, but pay off on the top line: Sales and Profit. It is important to keep the bigger picture in mind.
At this point, it is also worthwhile to think a bit about data strategy. Now that first-party data is more important than ever (keywords: Cookie Geddon and Apple Privacy Update), we should be clear about how existing data - possibly from different departments - can be combined and enriched over time.
The chosen strategy and the desired outcomes also determine the choice of the right technology. Therefore, it is worthwhile to define a longer-term strategy. This does not mean that it cannot be divided into short-, medium- and longer-term goals.
Marketing department going it alone
Marketing automation, as the name suggests, is a marketing discipline. However, this does not mean that you should do everything on your own. Marketing automation requires a rethinking of processes, so we should involve everyone who has contact with existing and potential customers in our project at an early stage and make every effort to ensure that we can recruit at least representatives from sales and customer service to our project team.
Successful implementation also requires professional change management - here, depending on the size of the company, someone from organizational development can perhaps provide support. It doesn't make things any easier if we bring a representative from the IT department to the table, but you save yourself a lot of trouble later on when it comes to issues such as data integration, data sovereignty or data security.
It's important to make a few friends in sales. Because those are the first ones who will jump all over the marketing department if they get no leads or bad leads. Besides the quality of the leads, the lead handover process is essential for the success or failure of marketing automation.
Feedback loops for further marketing measures are also critical to success. Leads that were hot from a marketing point of view and then aren't should not simply disappear into sales, but rather belong back in a nurturing program. In the better case, they are nurtured until they are really hot, or, in the worse case, until they are no longer leads.
But be careful: A bad process will not get better if it is automated. It is therefore enormously important that we talk to Sales about common goals and how we want to achieve them together, what the desired customers look like, how customer data is maintained, and so on. Only if both departments pull together can customers' purchasing decisions be positively influenced in a B2B environment. It's certainly a different story for consumer products that require little explanation.
Lack of segmentation and unclear target groups
Who doesn't know the holy trinity of marketing: segmentation, targeting and positioning? We should not simply ignore them when automating marketing. Unless we are moving in a homogeneous market and our target group is called "all companies in Switzerland". Maybe a few people will click on the ad or on one of the links in our e-mail. It's just stupid that they might be the ones we don't want to have and that they only generate expenses for the sales department.
For any form of marketing, it is important to have a clear idea of the potential customers or "buyer personas". Potential customers can be grouped into customer segments, while buyer personas define a segment per se. However, dynamic customer profiles are more suitable for marketing automation than simple customer segments or buyer personas. Customer profiles that are sharpened with each interaction. With modern marketing automation solutions, segments can also be created dynamically.
It is important that each message is as personal as possible ("hyper-personalized"). That's why we also talk about the "Segment of One", i.e. a segment consisting of a single customer. In practice, however, we plan campaigns that target coarser segments, for example, all customers whose (dynamic) profiles match criteria A and B, and who have viewed product X or Y online. Or simply on all shopping cart abandoners of the last 48 hours. Even in these cases, however, it is easy to work with text variables and messages can be highly personalized.
Different target groups can also be addressed with different measures and/or via different channels. This is another reason why we should think about segmentation. Depending on the target group, measure, and channel, we should definitely also ask ourselves what exactly we want to trigger with our action, what exactly we expect from the recipients as the next step, and what they need from us to move in the desired direction (target behavior). Messages and call-to-action must be dependent on this.
Lack of campaign planning
Modern tools make many things easier, but they do not release us from careful campaign planning. Here, too, we should start with the goals, with what we specifically want to achieve with a particular campaign. Possible campaign goals could be, for example, to reach the target group
- to encourage people to watch an explainer video,
- to download "gated" content (studies, whitepapers, tips & tricks),
- to motivate people to disclose their interests or to enrich their profiles,
- to encourage a trial ("Test now for free!"),
- for a webinar or physical event,
- to a request for a quotation,
- to entice them to make additional purchases (up-selling or cross-selling),
- recover after a shopping cart abandonment.
Once the goals are set, we should work on a rough campaign plan, a kind of roadmap. We need to be clear about the individual steps of our campaign up to the desired customer-facing action. Then it's on to the content: Email, forms, landing page, microsite, and so on.
The easier it is to transfer a plan into a practical campaign, the faster it is ready for launch. Good marketing automation tools offer a visual representation of the individual campaign steps in the form of flow charts that also show dependencies.
There is a widespread idea in marketing departments that it is enough to generate enough leads. It's a shame that the sales department has a completely different opinion. The expectation of the sales staff is that the leads are pre-qualified and the chances of closing a deal are correspondingly high. But what are good quality leads?
KPIs and lead scoring help here. For example, the result (score) is very low if someone has only opened an email (a possible, but rather nonsensical KPI). And slightly higher if the target person also clicked on a link (another possible KPI). But the score is really high only if the target person has not only clicked on the link, but has also engaged with the content on the microsite or landing page for more than a minute, has also watched an explainer video, and is not engaging with us or our offer for the first time, but has shown a high level of engagement before, for example, has also participated in a webinar. Good software solutions also allow third-party data to be considered for lead scoring. In a B2C environment, for example, data about a person's interests; in a B2B environment, for example, data about a company's search behavior (several employees have searched for product Z within the last X days).
When it comes to measuring success, two well-known quotes inevitably come to mind, attributed to several originators: "If you can't measure it, you can't manage it!" And, "If you measure it, you measure crap." The truth probably lies somewhere in between, though in the case of measuring open and click-through rates alone, it's more likely to be the second statement. And with what we measure, we indirectly influence the results - intentionally or unintentionally. The tendency is to optimize the campaign so that the set goals are achieved. But what's the point of high open rates if there's nothing in the end?
In addition to the NPS (Net Promoter Score), which is at the very end of the funnel, the conversion rates are among the really relevant KPIs. These should be measured at different points, typically from one campaign stage to the next. We should definitely measure conversions from Marketing Qualified Leads (MQL) to Sales Qualified Leads (SQL) and from SQL to Opportunities (sales projects) and from Opportunities to Wins or successful closes. This usually reveals areas for improvement, and marketers can make adjustments.
Touchpoint analyses are also important for marketing automation. It's worth analyzing at which digital touchpoints you create the greatest customer value and at which you can save the money for optimization. The following is worth reading this article by Christoph Spengler.
Tendency to perfectionism
You can spend a lot of time planning the perfect campaign. But it's been proven to start small, with a less complex, more manageable campaign. Mistakes can happen, but that's okay as long as we learn from them. It's better than spending a lot of time and effort on the perfect campaign, because the first lessons are learned very late in the process, and it becomes much more difficult to make adjustments to complex campaign structures.
So let's work with simple workflows first and learn what works and what doesn't within our target groups (through consistent measurement). Then we optimize and only later build more complex workflows with multiple triggers and lead scoring.
The motto is: Start simple and grow agile.
Lack of focus on the content
Bacon catches mice, content catches new customers. It goes without saying that content is central to any kind of communication. In times of over-communication and sensory overload, however, content is becoming increasingly important. In marketing automation, good content creates a pull effect (pull vs. push) and should therefore be a central component of any campaign planning. To do this, we need to ask ourselves what content could really add value to a buyer persona.
Good content should
- Address the pain points of the target group,
- be of high quality and not freely accessible,
- have a news character (vs. old wine in new wineskins),
- accommodate the information behavior of your buyer personas,
- include arguments relevant to the campaign objective, and
- be attractively designed.
Content is crucial for the success or failure of a campaign. As mentioned above, content should also take into account the information behavior of the targeted buyer personas. If several personas are targeted, this can also mean that the same content is available in various formats and on different channels - in longer and shorter versions, once more visual (video) and once more text-heavy (study, whitepaper), once for reading (article, blog post) and once for listening (podcast).
Clouded view of the customer journey
You may not be aware of the implications, but especially as "digital marketers" we should also deal with the most important aspects of customer experience management. This includes, among many other points, the customer journey, end-to-end processes across silo boundaries. We must not view marketing automation as a completely isolated discipline within marketing. We need to think in terms of holistic processes that reflect the entire customer experience - from the initial interest of a potential customer, through the purchase, to after-sales support and a loyalty program. Now, this doesn't mean that every campaign must necessarily encompass all of these areas. We should simply ask ourselves what impact it could have on the customer experience, right and left, and if necessary bring in colleagues from other departments at an early stage - see above.
With a good campaign, we can definitely make a positive contribution to the customer experience. Ideally, we gain "actionable insights" for better personalization of the next campaign or campaign steps. It is also important to enrich the customer profiles with newly acquired data, which will provide the entire organization with a clearer picture of the potential customers and enable the value proposition to be individually adapted to their needs.
Overestimation of data harmonization
In many organizations, there are not only departmental silos, but also many data silos. From my own experience, I know how difficult it is for sales people, for example, to deal with centralized data collection - to the point of actually boycotting it. The good news: there are customer data platforms (CDP) that make it possible to pull together data from different pools and harmonize it to a certain degree using AI. Or at least make it usable for marketing automation, provided we have the opt-in from our customers. This is also referred to as Customer Intelligence Platforms (CIP). However, just 25 percent of all German companies use a CDP (source: Marketing Tech Monitor 2021; n = 257), and these are certainly large mid-sized to large companies. There are no corresponding figures for Switzerland.
Under no circumstances is a lack of data harmonization a reason not to automate for the time being. Consolidating and harmonizing data usually takes far too long. And marketing automation per se, if set up correctly, will also contribute to a qualitative improvement in customer data. With each step of a campaign, customer profiles are sharpened. We can't get around a first-party data strategy anyway (Cookie-Geddon, Apple's Privacy Rules).
But what if we have practically no data on our customers at all? In this case, we start with the little we have. Triggers can be product purchases, birthdays or name days, product announcements, company anniversaries, etc. There is always an occasion - or transaction - for a marketing campaign.
The most important thing is that we ensure a consistent 360-degree view of customers, from every single department in our company. Also ensuring that the data is always up to date. The data must be easy to maintain at every point of customer contact. Depending on the use case, the data should also be synchronized with back-office systems (ERP, SCM), preferably in real time. After all, we don't want to advertise a product that has just been bought.
Much more could be written about data strategies, data management, data security, technology or even opt-in strategies, but it would go beyond the scope of this article.
Those who have the courage can get started as soon as the campaign is set up. And then perhaps be surprised that the set goals are not achieved. It is certainly better to carry out a few test runs before the starting shot is fired. Campaign strands, links, logic, intervals, etc. are checked for proper functioning by running through different campaign processes with a few colleagues. It quickly becomes clear what works and where improvements need to be made.
The test phase often also reveals whether the planned campaign can achieve the set goals. Of course, there is no guarantee of this. So if you don't want to put all your eggs in one basket, it's best to try A/B testing. This allows you to experiment with different campaign progressions, content, headlines, visuals, etc. Modern marketing automation solutions dynamically shift budgets to what performs better.
Non-compliance with data protection guidelines
Even if the European Union's General Data Protection Regulation (GDPR) and the Federal Data Protection Act (FADP) in Switzerland are unwelcome topics for creative marketing minds, you should definitely spend some time on them or get good advice - from external experts or your internal compliance department. Even if someone else is ostensibly in charge. While companies in Germany and the European Union are fined, often in the tens of millions, in Switzerland not only legal entities but also natural persons such as the marketing manager can be fined up to 250,000 Swiss francs if they have acted intentionally.
In any case, at least one person in a marketing department should deal with the topic of data protection, both with the previous, currently valid case law and with the efforts on the part of the EU, Switzerland and, depending on the market, other countries to further protect the privacy of their citizens (E-Privacy Regulation, Privacy Shield, etc.). This must be done even if one decides to use a professional software solution that supposedly takes into account all data protection-relevant aspects in the processes. This is not a guarantee that nothing can go wrong in this case.
Two other tips: First, I would make sure that only those employees have access to the data who have a "need to know" and know how to handle customer data. Second, I would always create full transparency for existing and potential customers. Customers should understand exactly what data is being collected for, what we intend to do with it, and how and where they can maintain the data themselves (newsletter subscriptions, opt-ins, etc.). If we create this transparency, customers are also more willing to disclose something about themselves, which later helps us to personalize our approach.
Preference for best-of-breed solutions
Many a marketer gets nauseous when it comes to evaluating the right marketing automation solution. The variety of AdTech and MarTech solutions available on the market is enormous. Scott Brinker of Chiefmartec.com, has been practicing for years to find a Overview to create; the latest from April 2020 shows around 8,000 solutions from hundreds of manufacturers.
So before you get lost in the jungle of features of possible solutions, you should be clear about what goals you want to achieve - and from that, what needs to be automated and what functions are required for this. There is a big difference between using a solution for email automation and personalizing content on websites, for example. It is worthwhile to look a little into the future. Too often, a tool that is well-suited to the task at hand is quickly purchased, only to discover later that it has reached its limits. You then need a second, and later a third tool, and then inevitably have to struggle with integration.
That's why you should perhaps ask yourself from the outset whether it really should be the best-of-breed tool that satisfies all wishes, or whether it wouldn't make more sense to opt for a platform that can be extended as required by various fully integrated solutions from the same provider. Here, it is also worthwhile to look beyond the end of one's nose. Is there a need for sales or service automation in addition to marketing automation? The chosen solution should at least offer adaptors for easy integration of third-party applications. Adaptors or open APIs are also important for integrating existing solutions, such as existing CRM and ERP systems.
Ideally, a good customer data platform (CDP) should be at the heart of any chosen solution (see above). This would ensure that data can also be managed effectively, access rights can be assigned, and data protection (DSGVO or DSG compliance) is guaranteed. In any case, we should create uniform customer profiles on a central platform.
Modern marketing automation tools offer the possibility to easily visualize the sequence of individual campaign steps with graphical elements and drag & drop functionality and to simulate the course of the campaign with a set of test data. The already mentioned A/B testing with dynamic allocation of budgets is also part of a modern tool. Not to be neglected in the search for the right tool are the options for data analysis. Here, too, simple solutions with graphical representation and "actionable insights" should be preferred.
"Last but not least, you should think carefully about who you get involved with. In addition to competent consulting and support during the implementation phase, attention should be paid to later support (accessibility, channels, self-service, language, costs). And especially with regard to later support, it tends to pay off to rely on a comprehensive marketing cloud from a single provider rather than on various tools from different providers.
Confidence in artificial intelligence
Artificial intelligence in marketing automation is both a curse and a blessing. On the one hand, in email marketing, for example, AI-based Send Time Optimization and Fatigue Analysis, i.e., the decision at what time of day and how often to communicate with a target person, lead to better results. On the other hand, algorithm-based programmatic advertising, for example, can lead to the exact opposite if the AI is incorrectly trained or targets have been set incorrectly, for example, if you are aiming for high click-through rates and a favorable CPM (thousand-contact price or cost per mille). Apart from the fact that few vendors want to see their ads on porn sites or the sites of extreme groupings, the wrong KPIs, for example CPC (cost per click) and conversion rate, can lead to sales being flooded with poor quality leads. High conversion rate in marketing, low to none in sales. Or nothing at all arrives in sales, although the marketing department celebrates high click-through rates. Quite simply because the ad server, due to the aforementioned objective (high click-through rate, low CPM), plays out interstitials on pages where there is little context and these simply want to be clicked away as quickly as possible by the annoyed website visitors. For a contextual reference, an interface to the CRM system suggests itself.
But it's also a curse because many marketers today are blinded by providers like Facebook or Google and lack transparency. After all, algorithms are a trade secret. It is therefore important to focus on achieving good end results, i.e. profitable business, and to define corresponding goals.
In any case, it's worth looking at the limitations of AI.
* Daniel Renggli has made his career in marketing and sales at technology companies such as IBM, SAP and Microsoft. Most recently, he was responsible for marketing Oracle's CX solutions for advertising, marketing, sales and service in six countries, including Germany and Switzerland. He is a federally certified sales manager and holds a Master of Advanced Studies in Marketing and Business Administration from the University of Basel. In addition to marketing automation, his professional interests include enterprise digital transformation, co-creation and customer experience management (CXM). He is active as an author as well as a speaker at events, and is also co-editor of the BeyondCXM podcast.