KPIs: Get more from your data
Is your marketing investment making or costing you money? Taking a more rigorous and strategic approach to your marketing data can give you a clearer picture of its impact and increase your odds of success.
Many organizations are awash in a sea of marketing data, and struggle to make sense of it. 87% of marketers feel like their data is underutilized, and 54% say that lack of data completeness affects their campaigns.
You need both data analysis and key performance indicators (KPIs) to accurately measure the impact of your marketing. Data analysis identifies the information you’ll use to measure the impact of your marketing strategy, as well as the tools and methods you’ll use to interpret it. Data analysis should be a key element in the design of your strategies and go-to-market plans. KPIs measure the effectiveness of those strategies.
How can your organization use KPIs to analyze your data and guide better marketing decisions? One helpful approach is to divide marketing data into three categories: descriptive (and diagnostic), predictive and prescriptive analytics. Here’s a closer look at each type and how you can use them to gain important insights about your marketing effectiveness.
Descriptive analytics provide a clear view of historic data to predict future marketing performance. It includes current customer data and previous engagement data.
How can this type of data help you? Let’s say you’re running a non-profit organization and you want to understand why you experienced a recent spike in donations. Descriptive analytics may include the donation date, the amount, the donor’s name and even the campaign to which they responded.
Diagnostic analytics take descriptive data a step further. They seek to answer the question, “Why did this happen?” Another term for it is root cause analysis. It helps you dig beyond superficial symptoms to understand the root cause – the “why” behind the “what.” Diagnostic analytics includes processes like data discovery, data mining, drill-down and drill-through analyses.
In our non-profit example, diagnostic analytics can be used to explore the data and make correlations. It may help you discover commonalities in your donors, such as where they live, their average age or the campaign to which they responded. You may discover that the spike in donations may have come from a recent outreach program you did at your local college campus.
Predictive analytics apply mathematical models to your objectives and the current data to predict future behavior. It is the “what could happen.” Once you’ve compiled your descriptive analytics, you can use a machine learning model to extract key trends and patterns. It helps you to make more accurate forecasts of marketing performance.
Examples of predictive analytics include customer value models, demand models and persona models.
In our non-profit example, predictive analytics could help you forecast an increase in donations at certain times of year the year, based on patterns in the data and donation trends.
Prescriptive analytics takes a wider view, incorporating a broad set of outside factors that could have an impact on the success of your marketing campaign. These can include economic, cultural, workforce, public health and competitive trends. You need to include all relevant external factors to model the most accurate forecast.
Prescriptive analytic tools apply a combination of machine learning, business rules, artificial intelligence and algorithms to simulate various approaches to these factors. It then suggests scenarios that may help you achieve your objectives.
Returning to our fundraising example, now that you know that donations are trending up, the prescriptive analytic tool may suggest you expand your campaign and follow up with similar outreach programs at other colleges. It may also recommend avoiding summer breaks and school holidays when students aren’t on campus.
Using analytics to set KPIs and move forward with ease
After examining your data through these lenses, you’ll want to choose at least three KPIs to measure success. They should go beyond the basics – such as the number of clients or donors in our fundraising example. What other data could help you understand what’s happening in your organization and how you can improve?
If you’re working in a digital space, there are numerous analytics that can help. For example, you can easily determine if a channel is over- or underperforming, identify your audience reach and pinpoint the messages that are resonating with prospective buyers or donors.
As you analyze the data, it’s helpful to model the economic impact versus the investment. Are you overspending with too little return? There are many ways to measure the value of your marketing efforts, such as marketing qualified leads (MQL), sales qualified leads (SQL), Net Promoter Scores and customer acquisition trends. Look at those factors and decide which ones are most relevant to your organization and your campaign.
Test your process and optimize as you go. Data analysis is your guide, but you may need to adjust your path as your customers and competitive landscape evolve. Use KPIs and the modeling methods we’ve described to create a more successful marketing strategy.
Want to know more about using KPIs and data measurement? Don’t miss our webinar series on how to get better Direct Marketing Results. In part five, we explore KPIs and how they can help you drive your organization ahead!