Make Quicker Decisions with Predictive Analysis

Believe it or not, predictive analytics are a part of our daily decisions. Tech is embedded in our lives in such a way that society increasingly relies on it. Going out for a run? You probably check the weather. That app uses data sets and algorithms to predict the climate in your area. For business owners, predictive analytics has the ability to make it easier for marketing to make more informed decisions. 

What does that mean, exactly?

AI, machine learning, and other automations take in data and analyze it in order to show what may happen in a certain amount of time. 

One example is a sales forecast. A software tool takes things like: 

• Past sales data
• Current marketing efforts
• The economy
• And other factors

That data is analyzed to predict what kind of revenue a company expects to earn over a certain period.

As a startup, the business environment is constantly changing. Internal and external factors threaten the longevity of your organization. Planning for contingency scenarios is the best way to weather any startup storm. This post aims to show how to improve your decision making with predictive analytics.

Defining Predictive Analytics

Predictive analytics is a branch of analytics that makes use of data, machine learning techniques, and statistical algorithms to predict future outcomes from historical data. It seeks to provide the best assessment of future outcomes instead of just relying on past events.

Through predictive analytics, an organization can use current and past data to forecast behaviors and trends. Many organizations already take advantage of this method of forecasting future outcomes.  This is because it helps to capture relationships among several factors to efficiently assess risks. Startups can also invest in this to give them a favorable playing field among their competitors in the marketplace.

Two critical factors when using predictive tech

• Good data in: At the end of the day, AI/ML software can only produce a result based on the information it’s been given. Lots of good data coming into the algorithm often means a more accurate outlook.
• Understanding the result: At this point, tech creates a great result (with accurate data). However, experience goes the rest of the way toward a good decision. Of course, having the ability to really understand what the reporting means makes the difference in using the analytics.

Directly Correlate Decisions to Useful Metrics

Predictive analytics is a few decades old. And it’s hitting a stride in terms of popularity in corporate circles. Analytics, in general, brings a number of benefits. Tech adds to those benefits, including the ability to sift through large data volumes to make quick decisions. 

Again, there are two main elements to using tech and predictive analytics—data in and the ability to understand the report that comes from that data. In the case of a business, most data comes from commonly tracked metrics, or key performance indicators (KPIs).

In order to show specifically how predictive analytics aids decision making, an example is necessary.

Example: A Software-as-a-Service (SaaS) company wants to create new products. 

Here are a few factors that will influence decision-making:

• A team of researchers: Here you need to consider how many people you need to design and implement the product. Also, you must consider if you will be able to meet the payment requirements for the team you are setting up.
• Sales and marketing budget: This will also require a team to help get the word out through marketing campaigns. Also, you have to create the kind of marketing campaign that suits the new product. Draw up a list of KPIs to measure the impact of your marketing team.
• Churn and lifetime value of the customer: Will the new product attract new customers? If it will, how will it affect the churn rate of your customers? This is very important for a business that is built on the foundation of customer subscription. Now, you need to weigh if this will affect the financial state of your company over the next year.
• R&D Costs: What is the overall cost of creating the new product? Will this amount be used in raising R&D Tax credit?

All of these must be put into consideration before the software company can begin developing its new product. Considering the large volume of data involved, it may take a long time to make a decision.

Tie Scenarios Directly to Metrics

Businesses depend on accurate data to make decisions on product development, marketing, customer service, and sales. Wrong decisions could undermine positive customer experience. It could also affect the number of conversions that a business makes since it may be chasing the wrong leads.

It is imperative to note that your decision-making is only as qualitative as the information backing it. This is why you must first be sure of the metrics that you want to measure. Let’s explain this point further by expanding on the software company we used earlier. 

Here are some of the metrics that such a company should measure:

• Bounce rate
• Churn rate
• Customer Acquisition Cost
• Customer Lifetime Value
• Customer Satisfaction Score
• Daily/Monthly Active User ratio
• Monthly recurring revenue
• Net Promoter Score
• Number of sessions per user
• Number of user actions per session
• Retention rate
• Session duration
• Traffic (paid or organic)

Without predictive analytics, it could be very difficult assessing all these metrics to make quick decisions. Predictive analytics helps you quickly figure out how each of these metrics will affect your company in the immediate future. 

For example, if the churn rate increases, how does this affect your monthly recurring revenue? How do these metrics affect your marketing avenues? Will you be needing extra marketing reps? If yes, can you afford it at the moment or you have to acquire some more funds from sales first? Are you meeting your sales target per month? What will happen to your company if you don’t meet this target?

Predictive analytics makes it easier to answer all these questions so that you can make quicker decisions. Remember that the survival of your company, especially at the startup stage, is highly dependent on the quality of your decisions. One wrong decision and you could watch your company crumble like a house of cards.

Put Contingencies Together Now

One characteristic that is common to entrepreneurs is “intuition.” That’s the ability to make decisions from a gut feeling. However, this is not enough to keep your company afloat. You must also have the ability to access and assess data to make forecasts and save your company from potential errors.

Why plan for the worst and hope for the best, when you can plan for both or even the unknown simultaneously? How is this even possible? Through predictive analytics. It places all your cards before you so that you can make smart decisions to keep your company running and achieve success.

Detailed Forecasts from Founder’s CPA

Getting a grasp on predictive analytics takes time to set up and implement. Our experts understand the importance of both financial and non-financial metrics in the context of fast-growing startups. We are geared towards helping startups to put together forecasts using predictive analytics. 

Have any questions? Reach out to Founder’s CPA, for a free consultation.