Sending the the right content to the right person at the right time. A simple concept, but a complex task. Assuming you have a marketing calendar, you already know the ‘what’ and ‘when’ of your marketing campaign. But who is the right person and, on a larger scale, the right audience for each of the messages you have prepared? A better understanding of the term ‘campaign targeting’ will bring us closer to the answer.
Definition: What Is Campaign Targeting?
Campaign targeting is the process of selecting a subset of your existing contact list to maximize the ROI of your marketing campaign. Next Buzzword, next definition. When we talk about return on investment on marketing campaigns we want our contacts to engage with the campaign so that, utimately, they end up buying our product or service. This is still a vague interpretation and not yet a clear definition, but we hope you get the point. Taking the case of email marketing, high customer engagement is often measured through open- and click-through rates, that are then assumed to result in increased sales. This effect might not always be a direct cause. Imagine sharing inspirational content with potential customers so that they start to trust you and see your brand as authentic. But not every customer will be engaged by the same content, and so different variants of a message will have to be matched to different types of customers. Segmentation strategy, email segmentation and contact list grouping are concepts used interchangeably to refer to this problem – but these terms define the solution, not the practical approaches to solving the segmentation problem.
Now that we know what campaign targeting or segmentation is, we’re obviously interested in what benefits it delivers.
Why Target Your Campaigns Better?
Let’s tackle this with an example. You will probably agree that sending an email about the newest trends in sneakers to a list of young 15-25-year-old contacts is better than sending the same content to an elderly audience. But what about the 20-year-old nature lover who is not interested in fashion? What about the 40-year old mother who is particularly keen on sneakers? Clearly, such simplistic static segmentation will miss many opportunities, and unnecessarily harass many other contacts, too. What we need is dynamic segmentation, and focused campaign targeting. In particular, seeing contacts tired of receiving irrelevant content opting-out of your contact list scares every marketer out there. (Hold on, we are with you!) Think about it for a moment – how many irrelevant or superfluous promotion mails did you receive within the last month? To give an example from my personal life – just the other day I received a promotion for a suit that I had already bought. This is a waste of marketing resources and we can do better, way better!
So far so good, but how can I actually benefit from using campaign targeting? According to the MailChimp study on the effects of list segmentation on email marketing stats targeted campaigns will generally outperform non-targeted emails. More than 10% higher unique open rates, 100% higher click rates and about 9% less unsubscribes. I think you got it, to us it is just common sense to target your campaign to the right audience!
So now you want to benefit from optimized campaign targeting! But how?
How To Optimize Campaign Targeting
The more information you collect about your customers and your leads, the more advanced your campaign strategy possibilities are. Below you will find an overview of strategies and best practices you can implement to increase the chance of finding ‘that right audience’ for the content you have planned. Targeted marketing and targeted advertising require some effort to set up but can be automated so you will end up having more time to focus on creating meaningful content.
- Keep your customer relationship management data up-to-date This step is crucial to perform a decent email campaign targeting. Your CRM ideally is the single source of truth in terms of customer data and defines a unique identifier such as email address for every contact in your list. To really target your campaign efforts your CRM data has to be up to date, always! The basic information such as gender, age and location offer a good starting point in terms of targeting. If you are using machine learning techniques make sure to timestamp your contact updates, and keep the history. This will enable you to use the historical data to evaluate strategies using the information you had at that point in time. Conclusion: Keep your CRM data updated, have a unique identifier for each contact and use this information!
- Ask for contact interests Rather than using all of the data you have collected about your contacts and combining it with their website interaction to extract their fields of interest you can simply ask for them. Sounds trivial right? You need less maintenance to set up a ‘What are your interests?’ form as when you set up static rules that categorize user interests that might change over time. Prominent examples who implemented this strategy are Amazon and Medium. Specifying your interests on Medium will help them to recommend potential posts of interest to you. Conclusion: Simply ask for what you need!
- Purchase history Nothing feels less personal than receiving advertisements for products and services you have already bought. So please prevent this from happening to your customers. Your sales database (hopefully combining online and offline sales) will provide a purchase history for all of your uniquely identified contacts, so you can easily filter all contacts who already bought that item you are about to promote. The decision to include a contact in a segment should be driven by the measured correlation in your data between marketing message frequency, and the resulting purchase behavior. In addition, it is well-known in marketing that higher-priced products need more contact points with customers before they convert. But there is more: Sales data is invaluable to determine the optimal personalized up-selling and cross-selling strategy for your services and products. The results of these computations can then, for instance, be used for dynamic content blocks in marketing messages. That’s an example of personalization, not campaign targeting, but these go hand in hand. One might now remark that this is an example of personalization, not campaign targeting, but in practice these go hand in hand: A personalized recommendation is nothing more than a one-person segment!
- Website behavior Website visitor tracking allows you to analyze the user behavior and identify users across sessions. These data will be to your benefit if you can distill from it the events or goals a user completed on his journey on your website. For example, if you know that a contact from your list searched for products in category A and engaged with the items in this category by reading the detail pages, you might consider him to be included in the next promotion or update on category A. For this reason, we recommend setting up a definite set of website goals that are tracked per user and use event trigger data to determine their segmentation membership. Conclusion: Customer behavior on your website can give detailed insights in his interests and position in the sales funnel that help with segmentation decisions!
- Previous campaigns One of the best ways to inform the decision whether you should include a contact in a segment, is to learn from engagement with previous campaigns. When a customer never engaged with the campaigns you sent him for a specific topic, you might consider him not interested. On the other hand, he might be interacting with a specific niche topic for Black Friday sales all the time. Make sure to use this information to avoid spamming users that will never be interested in some of your content. Conclusion: Analyze previous campaign engagement to refine segmentation!
- Use customer lifetime value Another buzz word in marketing and indeed a useful measure to optimize campaign targeting: the predicted customer lifetime value (CLV). The CLV metric calculates the future worth of a customer for your company. By computing the individual CLV, and your envisioned campaign’s potential impact on it, you can decide for each contact whether their inclusion in a segment would increase your ROI or not. CLVs are of course always just an estimation, but nevertheless they turn out to be amazingly efficient guides to the more succesful campaigns. Conclusion: Take your company’s data scientist and make sure you calculate customer lifetime values!
- Learn continuously It is a great idea to track, analyze and a/b test all of the data mentioned above. But to get the maximum benefit from these techniques, you will have to religiously iterate on extracting business rules and patterns over time, fine-tune and actually learn from the feedback metrics. This is a tedious task, so you would want to automate the process of targeting your campaigns as much as you can. The self-learning marketing assistant uses the power of predictive marketing to bundle the techniques we have discussed in this article, automatically predict the success of any campaign, and continuously learn from your data – so it can always extract the optimal target audience from your existing contact list. To find out more about applying machine learning to automate the campaign targeting and segmentation process, have a look at the Selma concept paper. Key takeaway: Patterns in data will change over time, so automation will save you a lot of repeated effort!
Conclusion And Follow-up
Targeted marketing campaigns are a crucial part of your overall marketing strategy: To not just send personalized content but also actually relevant content is key in sustaining customer happiness, and not losing leads. Setting up all the integrations and combining data to get maximum insights is worth going the extra mile. The benefits of implementing extensive campaign targeting strategies include more happy customers, less unsubscribers, and most likely reduced campaign sending costs – because you can now avoid sending mass mailings to your entire contact list. Do not overlook, however, that successfully implementing these strategies implies continuously learning from your data, and experimenting with your segmentation!