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The marketing trade press is now full of case studies showing how AI solutions can make game-changing predictions of customer behavior, driving both efficiency and profitability.
These solutions are predominantly tailored AI applications, focused on predicting probability across lead conversion, customer churn and cross/up-selling of products and services. In doing so, they enable sales and marketing functions to drive positive outcomes through proactive decisions rather than having to react to sometimes negative situations.
For many years now, Luxid has been applying the underlying components of popular AI tools in our work to improve business processes in marketing and sales and to drive customer experience. These components are machine learning models - powerful analytical methods that allow us to make predictions based on data.
So given the success of these types of AI solutions, why aren’t more marketers adopting them? One of the key reasons is that AI solutions can be expensive as there are very few off-the-shelf products. Every company has unique data sets which makes it difficult to create standardized products that accommodate everyone.
Then there is the unpredictable nature of custom AI-solution projects and the uncertainty of whether they will actually deliver ROI. One of the key factors influencing the uncertainty of AI projects is data insecurity. Creating an AI solution often requires large amount of data – preferably over many years. However, unregulated data collection practices can lead to inconsistencies, for example a business or system change can significantly affect the meaning of CRM data. Such inconsistencies make it difficult to teach an AI solution to make predictions using that data.
At Luxid, we are able to overcome these data challenges by starting with a proof of concept (POC) to validate the AI solution’s business value. With our colossal amount of under-the-hood system knowledge - particularly in marketing automation, CRM and sales platforms - we are well-equipped to deal with data insecurity. Through our approach, we’re able to predict the risks and find the inconsistencies in the data, avoiding the major pitfalls of creating a tailored AI solution. And, crucially, our expertise allows us to perform this process very cost effectively.
We follow a tried and test process for AI solutions:
- Define the business value proposition of the AI solution and create a plan for how the solution should work
- Kick off the AI POC project and perform EDA (exploratory data analysis) while testing the AI solution
- Validate the finalized AI solution with real-world data
During the project we can determine if there’s enough good data to create an AI solution that performs as defined in the business value proposition. Once the validation step is performed and the solution is shown to be successful, we can start developing a project plan for how we can automate the model in your technical ecosystem. This means the AI solution will be integrated into your system, providing the predictions at a set interval (usually once per day) into your end-user systems, such as the marketing automation or CRM platforms. The tools used for this often include cloud platforms, which we are able to set up for you.
Resolving data insecurity is critical for the success of a tailored AI solution. And that is why starting with a POC is so important. A POC will establish the integrity of your data. A POC will minimize costs, focusing on agility and the least of amount of time to build a solution that delivers ROI. And by starting with a POC you can safely create an AI solution that can be scaled throughout your organization.
Are you looking to leverage AI for marketing and want to get the most from your data? Drop us a line and let us see what we can do – you lose nothing by asking.
