How Machine Learning Plays a Role in Multi Touch Attribution

Machine Learning Multi Touch Attribution

In a fast-moving, ever-changing consumer market, companies that want to stay ahead need to be quick to adapt. On the other hand, those that don't take advantage of innovation can be quick to fail.

The rise of artificial intelligence in digital marketing has led to machine learning multi touch attribution, a new form of analysis that can revolutionize the way you strategically plan campaigns.

Increase Efficiency with Machine Learning

Machine learning is a form of artificial intelligence which elevates the application of basic statistical techniques. With a machine learning model, you have a computer system that has the intrinsic ability to learn and improve its performance as more data is inputted and analyzed.

The quick and efficient processing capabilities of machine learning models are effective even without being explicitly programmed to analyze one campaign or another. It is especially more efficient when you compare it with traditional methods.

Getting analyses in the old method – through econometric models – requires intensive human labor and a less dynamic lens. For example, marketing analysts need to perform logistic regression on existing data, to get the input needed to apply a particular model. The traditional method has several constraints, as analysts need to make assumptions on which model to use, to correctly convert data into its relevant form, and to do all adjustments in analysis within a short period of time.

Instead, you can invest in artificial intelligence. Machine learning systems can measure and account for different situational variables on the fly, and adjust for them accordingly. Instead of taking the time to make assumptions, machine learning models can quickly run through different models to successfully pinpoint which marketing interactions matter. It's the shortest path to success.

Any marketing campaign planned with the analyses from machine learning models automatically become faster and more responsive.

Multi Touch Attribution for Effective Marketing Analysis

Machine learning models, like man-made analyses, can generate many kinds of analyses and graphs. The key difference is in speed and precision. With machine learning models, you can expect true attribution aside from increased efficiency.

True attribution means you can pinpoint which marketing activity is responsible for a particular outcome. In most cases, marketing analysts try to save time by looking only at proximal factors. For example, a successful sale is seen as a direct consequence of a marketing advertisement that was flashed right before the transaction.

However, sales and transactions are rarely that simple. Success needs to be attributed to its proper causes, which can involve a long-term and non-immediate marketing activity. Often, determining the correct attribution requires more processing power than what traditional methods can offer.

Machine learning multi touch attribution can answer the real questions for you. Which marketing activity or activities have the greatest impact? Which ones have the lowest return?

These are the kinds of ratios that can turn sale activities into data points for strategic planning. Aside from being as close as possible to the truth, machine learning accounts for multiple touchpoints. Success can be due to any number of marketing activities; a series of touchpoints may have been integral to the conversion of a sale.

With multi touch attribution, you can speedily recognize multiple marketing touchpoints that you may have otherwise missed.

Improve your Business with Machine Learning Multi Touch Attribution

Machine learning multi touch attribution already promises faster analyses that can precisely pinpoint multiple marketing touchpoints behind an actual sale. This is the kind of analyses that can improve your company's strategy. With this information, you can cut losses by re-evaluating underperforming campaigns and gain more by promoting successful ones.

Machine learning can also lead to better "what-if" analyses. Instead of predicting and assuming trends based on cluster analyses, which is heavily based on correctly identifying and grouping prospects and customers, a system can run through different conditions for you. If a particular situational element has changed, or if you redirect efforts to a certain marketing campaign, what is the impact to your bottom-line?

With machine learning multi touch attribution, you can raise your bottom-line and remain a cut above the rest. Innovation is the key to creating a more competitive foundation for your overall business strategy.