When managing a business, KPIs can help provide insight into the business’s current health and past health. But what if you could use the data available to predict what KPIs will be in the future based on certain business decisions? With data science and machine learning, predictive analytics can be a reality for your business.
In recent years, Artificial Intelligence (AI) and the amount of information available have grown exponentially, making integrating newer technologies into your business seem daunting or expensive, but it doesn’t have to be. AI uses data available to predict the outcomes of different business decisions on different levels. With the right models, AI can predict current customer preferences to help drive product development and forecast future demand.
As a CFO or business owner, you may find some hang-ups incorporating data science into your business strategy and reporting. With many other aspects of the business pulling your focus, you might find you’re continually attaching the label “later” to the project. Even if it’s not front of mind, it is an important task that could help you make better business decisions. In addition, the cost and time associated with implementing forward-thinking KPIs into strategy aren’t as expensive as you might think because jumping right in with a complete overhaul of your KPI dashboard and reporting programs is unnecessary. Getting started can be simplified by following the below steps.
While it may seem intuitive to give the data scientists and IT team cart blanche in implementing the AI programs, telling them to do what they do best, it’s essential to include the people who know your prospective client the best, as they are better versed in what questions to ask to get the answers they need to improve strategy. Look to your marketing, business development, and revenue teams to help guide this direction.
Implementing new predictive data science into your business strategy can be a massive, time-consuming task. Start by identifying the most critical KPIs and working with data to help move those numbers. Once you’ve identified your metrics, check if other businesses have tracked those metrics previously. There’s likely a framework you can follow instead of starting from scratch with data like transactional information, web analytics, and social media, saving you time.
Most marketing teams share similar challenges and goals when obtaining new clients. Focus on how your team measures ROI regarding acquisition, retention, and engagement and use that to generate future predictions. From there, analyze clients by predicting their lifetime value a few days after acquisition, then again at 7, 14, 30, and 180 days.
Once you have started to look at predictive insights to impact core KPIs, continue to look toward the future instead of falling back into habits of reviewing a snapshot of the past. Doing so will allow you to make decisions on which types of clients to focus on and where to invest your marketing and business development resources.
Embracing the recent advances in data science can help more efficiently direct your business’s time and resources to tasks that will improve your business’s performance.
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