In this Article
Predictive campaigns use historical data to forecast customer behaviour and thus proactively adjust strategy and next steps. AI greatly assists in it, as it helps process an overwhelming amount of data in a short time and makes more accurate predictions. In this article, we dive into the topic and discuss all the aspects of AI optimization – how exactly it benefits your marketing and what use cases need AI, what tools to use, and what challenges may appear.
How AI takes predictive campaigns to the next level
Predictive campaigns didn’t appear with AI; they were here long before and relied on analyzing historical data to forecast possibilities. However, they relied on basic segmentation, rule-based logic, and manual identification of behavioural patterns and predictions. They weren’t personalized, and you had to spend a lot of time organizing data before feeding it to apps and platforms for analysis. As apps used only historical data without considering current changes, they couldn’t dynamically adjust their predictions to fit the present. Complex patterns had the tendency to be lost, as well as accuracy, especially when scaling.
The appearance of AI changed things. Machine learning models not only analyze way more information faster. They can deal with both structured and unstructured data, getting you rid of the necessity to organize data beforehand. And they consider not only historical data, but current situation to predict changes dynamically, adjusting their forecasts and marketing strategy in the process. Over time, such models get even more accurate, as they train and learn. They can spot and analyse hidden patterns – something that before-AI apps can’t do – and predict numerous things:
- Probability of purchase
- Churn risk
- Next best piece of content/channel/offer
- Optimal send time
- Price sensitivity
In a nutshell, AI helps with such things:
- Precise segmentation
Before, you created general segments like “Purchased in the last 30 days”. AI updates segmentation automatically, based on current behaviour.
- High-grade personalization
AI helps with tailored recommendations for each customer – including product recommendations, communication tone, best channels, and timing. Netflix or YouTube recommendations are examples. Their algorithms learn what you watch and like, calculate how much time you spend on different content, and then tailor recommendations.
- New customer acquisition
Acquiring a new customer follows the same scheme: identify your top 5% LTV (lifetime value) customers and work to target the same people in your campaigns. However, before it was on a “they are the same age” level, and such targeting was often imprecise. Now, AI algorithms look for people with the same behavioral patterns across numerous variables. The probability of them buying from you rises, and CAC (client acquisition cost) lowers.
- Conversion path analysis
People rarely buy after one interaction. They see your ad, visit your website, then see your ad again, then receive your email, and here is where a purchase is possible. However, which of those channels lead to a purchase and which were useless? Earlier, all touchpoints received equal credit. Now, AI calculates it – using chain modelling, media mix modeling, etc. Basically, it experiments and simulates different situations – for example, it removes one of the channels. Imagine a situation: you see that 70% of your buyers interacted with YouTube before purchasing, but only 5% of them converted directly from the platform. With the classic model, you would have stopped using YouTube. However, with AI simulations, you can see how huge or small a drop in conversions it may cause and decide. AI helps identify such indirect signals and prevents underfunding or overfunding of channels.
- Automated decisioning
AI determines the “next best action” for each individual itself, saving you time.
Those insights improve who you target, how you do it, what channels you use, and how you invest money.
Best AI tools for predictive campaigns
There are a lot of instruments, and also the choice depends severely on your industry (SaaS, eCommerce, etc) and budget. However, there are several heavyweights that hold the top position. For convenience, we group them into categories.
Marketing Automation Platforms
Salesforce Marketing Cloud with Einstein
Its AI is called Einstein for a reason – it’s embedded natively inside CRM. The tool provides a 360-degree customer view, Journey Builder with predictive splits, recommendations for content and product personalization, and deep integration with other clouds, such as Sales and Service. As a result, you have a clear understanding of the CTV of customers, probability to buy, churn risk, and next best step. It also performs real-mode segmentation and scoring.
Adobe Experience Platform with Sensei
This is an enterprise-level option for content and experience personalization. It profiles costumes in real time and calculates the possibility of purchase. The tool is especially good for cross-channel marketing. Integration with Adobe Analytics and Campaign provides you with a full understanding of the effectiveness of your actions.
This platform was built for middle-market e-commerce brands, and it offers easy Shopify/Magento integration options. It offers important predictions like LTV, churn risk, or expected next order date. Predictive segments show on visual dashboards.
Customer Data Platforms (CDPs)
The platform collects data from various touchpoints, from websites to CRMs. As soon as a customer does any action, be it a click or signing up, the tool updates the customer profile, connects the user ID, email, etc. into one unified profile, and sends that fresh data to AI models for predictions. Segment is easy for tech teams to connect with others, and as it connects with ad or AI platforms, it helps with identifying your ideal customer and finding the target audience.
A solution for large enterprises that use many channels, this platform is flexible and works in real-time mode. It cleans and enhances incoming data from touchpoints automatically and then updates customer profiles. You can create segments and score audiences also in real time. Another reason Tealium is good is that it tracks users across server-side systems, web, and mobile apps.
Media optimization tools
While being free, the platform is scalable and offers important predictive metrics like purchase probability, churn risk, and seamless integration with Google Ads.
The tool combines deterministic and probabilistic approaches and provides channel contribution analysis and budget usage insights.
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The platform uses AI to optimize budget spending and predict the best placements and creatives. Among its features are a unified campaign structure and intent signals for predictions. GPM works across Search, Display, Discover, Gmail, and YouTube.
Machine learning platforms
Those tools aren’t necessary; it’s a choice if you need custom modeling beyond standard vendor AI capabilities.
The tool handles huge amounts of data within one system and helps you build sophisticated models for churn predictions, LTV, and next best offer calculation. Basically, it combines AI, data storage, and processing in one. This makes the app good for teamwork.
This is an all-in-one solution: it allows building, training, and launching AI models, even if you’re a newbie. The app works well with BigQuery data, offers a lot of features, and provides the AutoML option for non-tech teams.
Challenges of AI optimization
There are some hurdles you may experience while adding AI to your workflow:
- Data quality – any model is as good as the data you feed it. Accurate, reliable data is your target, and getting it may be hard. If data is biased, incomplete, outdated, etc., it’s of no use.
- Unstructured data – information that is scattered across systems is way less useful.
- Privacy concerns – while getting and using data and training your model, you must obey data regulation laws such as GDPR and others; in Europe, the EU AI Act is coming as well.
- Lack of transparency – sometimes it may be hard to explain or understand why a model made this or that decision.
- Deployment problems – you need to train your teams.
- Additional costs – to be able to use AI, you need ML engineers and up-to-date tech infrastructure, which may require investments.
Final words
AI surely takes prediction campaigns to another level, allowing you to reach such a degree of accuracy and scalability that was impossible with classic tools or manual analysis. However, another challenge appears here – data. You need high-quality data to train your model and to give it something to work on. At the same time, you have to stick to laws while obtaining, using, and storing data. Here, DataImpulse can help you. With our ethically derived, GDPR-compliant residential proxies starting from $0.7 per GB, you can gather data smoothly and fast. Contact our support team or hit the “Try now” button to start.
