During a crisis, marketing managers must show how they help the company succeed. They need to prove their work brings value so they can keep their budgets. Analytical AI helps them do this. It clearly shows which actions work best on each marketing channel. It also helps adjust strategies quickly. Study says solving this challenge has been very difficult until now.
 |
| In turbulent economic times, Analytical AI helps bring clarity and control to the marketing mix |
In a troubled economic environment, marketing managers face serious challenges. More than usual, people expect them to create high-quality plans and clearly prove their value. This happens because, in times of fewer rules and budget cuts, companies demand better efficiency and higher employee productivity.
This pressure applies to B2C marketers (see Case Study Vodafone), B2B marketers (see Case Study Avnet), and their agencies (see Case Study Media Plan). All of them must show how their marketing efforts directly lead to strong results.
That’s why marketing managers must link reliable earnings indicators—like extra sales or increased revenue—to their actions. They must identify what causes success and keep improving the marketing mix based on those insights. At the same time, they must find these insights faster and use fewer staff. This helps them react quickly to market changes and find ways to improve without delays.
The problem of measurability – with and without cookies
For many years, advertisers believed that switching to online marketing and e-commerce would solve everything. They thought cookies and tracking tools could show how their campaigns performed and help them run ads automatically.
But that belief is no longer true. Clicks and conversions don’t fully show the real return on ad spend (ROAS). Ad fraud and overreporting also make performance data less reliable. It's hard to trust the numbers now. Measuring the success of online campaigns is becoming more difficult. Big platforms like Google, Meta, and Amazon are closing off their systems. Privacy laws also limit how much tracking advertisers can do.
People now spend more time on mobile devices, smartwatches, and voice assistants. This shift has made digital ads less engaging. People pay less attention to digital ads. This reduces how deeply ads can influence them. Companies now see that they also need offline ads. These ads don’t fit into simple, digital tracking systems. But they are still important and can have strong impact.
Statistical and stochastic attribution: the superpower of the geeks
But what can marketers use if they cannot rely on fixed paths or basic data? The answer lies in using statistics and modeling more deeply. Marketers who manage their own data well can see how much sales, revenue, or brand awareness they already gained—without doing extra work.
They only need longer records of the right data. This data often comes from CRM systems, server logs, or regular customer surveys. These records show patterns over time. With time series analysis, marketers can find trends and seasonal patterns. Then, with tools like variance and regression analysis, they can see outside influences. These include factors like customer buying power, competitor advertising, or even the weather. All this analysis helps marketers create a clear and realistic starting point for their strategy.
Case Study: Vodafone
Vodafone uses a multi-level sales system. It includes company-owned stores and partner agencies. Alongside these, independent retailers also sell Vodafone products through a decentralized network. Vodafone can easily manage its own marketing campaigns for IT and data-focused customers. But tracking and improving advertising in indirect retail channels is much harder. To handle this, Vodafone automates its advertising processes. Through the Vodafone Connect online portal, independent retailers can start and manage online ads. They can also set up fully automatic campaigns for specific time periods.
The NEUTRUM platform powers Vodafone Connect behind the scenes. It handles the planning of all advertising actions on the portal. NEUTRUM also offers demand forecasts and gives suggestions to improve the campaigns. This setup forms a smart AI system. It studies how different retailers perform and learns from their actions. The AI bundles campaign data and helps improve budgets, timing, and planning for future marketing.
It tracks interactions and adjusts plans to make marketing goals more realistic and easier to reach over time. If an advertising campaign really works, it's easy to see the result. You will notice more sales and growth compared to before. If sales clearly go up, and nothing else changed, you can often link the success to the campaign with confidence.
This sounds simple: if a campaign helps, the improvement must show in the results. You should see better performance in your target numbers, like sales or clicks. But in real life, it’s not that simple. Campaigns often run across many channels at the same time. This makes it hard to know which part of the improvement comes from which campaign.
That’s where experts come in. They use tools like Bayesian models. These models help them understand complex patterns and connections. Experts use these models to figure out how much effect a specific activity had. They use statistics to separate the impact of each campaign. This helps them say with more certainty which campaign caused which results.
Who masters this art, can ...
Marketers must question what happens outside the control of tech giants’ closed platforms. For example, we expected to generate 1,000 leads. But we don’t see them in our forms, contact center, or shop. Where did they go? We need to track those leads clearly using cookies or IDs, even across these closed platforms.
We must measure the impact of channels that don’t have direct performance tracking. For instance, do we notice more people visiting our stores when we use large poster ads across cities? Up to what point does this effect last after we remove the posters? We need to test and measure this carefully.
We must also understand how different channels work together. For example, do our affiliate partners get more conversions when we run TV ads at the same time? If yes, do those extra conversions make the TV ad cost worth it? We need to calculate that.
To answer all of this, we must connect our own data from ERP, CRM, and analytics tools. We also must match it with detailed data from our ad campaigns. Only then can we measure real effects and make better decisions.
AI as Game changer: Learning models show the way
If this method works so well, why didn’t we always use it? The reason is simple. These calculations need deep knowledge, and even trained statisticians and data scientists find them hard. Building such models takes a lot of time, effort, and money. It’s not a quick or easy job.
The same problem happens when you try to combine First, Second, and Third Party Data from different systems. People who try to gather data from tools that export it in different formats face many problems.
They have to clean, match, and align the data to make it useful. This task takes patience, time, and focus. It often becomes frustrating, feels petty, and leads to many small errors that ruin the results.
Case Study: Avnet
Avnet is the largest distributor of high-tech electronics in the world. In the EMEA region, its companies Avnet Abacus and EBV Electronics supply parts from over 160 manufacturers. They promote these products through more than 100 channels. These include online, offline, live, owned, earned, and paid channels.
To improve their marketing and show clear results to manufacturers, they must track which actions create which effects along the sales funnel. In the past, people performed this analysis using descriptive statistics at set times. This method took a lot of time and effort.
Now, they have automated the entire process in real time. They did this in just 20 months using the Analytic AI system called NEUTRUM. This system uses AI models that can learn from data. It allows benchmarking and attribution. It can also simulate how budget changes affect results.
NEUTRUM gives useful suggestions to improve marketing based on this data. It shows what works, what doesn’t, and how to adjust for better results. Analytical AI is like the responsible sibling of generative AI. It changes how we work with data. It can automate both data integration and modeling. It does this with human-level quality, no errors, and much faster—thousands of times faster.
Explainable AI tools show how the models work at every step. This helps meet all reporting and compliance rules without extra effort. Analytical AI also learns in real time. It uses fresh data from campaigns, the market, and even its own past predictions. This helps the model improve continuously without waiting for new updates.
This gives advertisers two big benefits. First, they can now optimize and evaluate campaigns more often. Earlier, they did this only once a month or less. Now, they can do it continuously. More frequent optimization means faster learning. Faster learning brings better results. This leads to higher efficiency and smarter campaign decisions.
Case Study: Media Plan
Media Plan is a well-known media agency. It works with big brands like Asam Beauty, Hello Fresh, and Weleda. The agency plays a key role in today’s fast-changing advertising market. Media planning is becoming more complex. Online marketing is getting harder to understand. At the same time, advertisers want quick returns on their spending (ROAS).
To solve this, Media Plan created its own AI framework. It built this tool using the NEUTRUM system. This AI helps design smart media mixes for complex campaigns. It works well even when many media channels and breaks are involved. The AI can also measure how valuable the advertising is. It keeps the quality of the model high, even in setups with many moving parts.
Most importantly, the AI gives clear advice on how to split the ad budget. This helps clients make better decisions. With this system, Media Plan gives its clients full transparency. Clients can clearly see how decisions are made. They also get detailed reports with results they can trust.
On the other hand, each optimization loop improves the model’s quality. As a result, the learning curve rises faster and faster. This allows people to use optimization tools more efficiently over time. Within two years, these learning models become more than twice as efficient as models that do not learn.
Conclusion: Let's get back control
Baseline modeling, media mix modeling, and stochastic attribution were expensive tools. Most companies could not afford them. Even big advertising firms treated them like costly experiments. But AI with smart automation now makes them available to almost everyone. These tools need fewer people and less money to run.
AI runs these models automatically. It keeps learning from new data all the time. This constant learning turns one-time reports into live tools. Companies no longer just measure ROAS. They now actively improve their marketing mix every day with AI’s help.
This is the only way marketers and platforms can truly understand value. You can check key factors yourself, like budget, timing, and location. Your own company already has the other needed data. You can find it in Web Analytics, Contact Center Analytics, PoS systems, CRM, and ERP tools.
For the first time, marketers can reduce wasted budget. They can also measure their value clearly and improve it over time. The only things needed are the right AI tools and a strong will to create transparency.
Comments
Post a Comment