One of the most dramatic and pervasive changes I’ve seen in marketing over the last 20 years is the prominence of a marketing information system guiding marketing decision-making and planning. In general, marketing is more data-driven, especially in supporting the ROI (Return on Investment) of marketing actions. The old days of running your marketing, especially social media marketing, using intuition are gone. Now, you must support the contribution of marketing actions to supporting the goals of the firm.
What is a marketing information system?
A system that analyzes and assesses marketing information, gathered continuously from sources inside and outside an organization. Timely marketing information provides basis for decisions such as product development or improvement, pricing, packaging, distribution, media selection, and promotion. — Business Dictionary
- a marketing information system is a continuous process of data collection while marketing research
- a marketing information system is designed to guide everyday marketing decision-making and planning, while marketing research is commonly conducted to address a particular marketing problem facing a firm, such as will enough consumers buy a new proposed product offering.
The marketing information system process
Collecting, analyzing, and disseminating marketing information relies on a the XX steps in the process.
Step 1. Determine what metrics to include in your marketing information system.
This is a very serious step in creating an effective marketing information system. All data has a cost, both real and opportunity costs, so including the RIGHT metrics is critical.
- Measure the wrong things and you’ll make bad decisions and waste money. An example is measuring fans to your Facebook page. Sure, there’s a slight advantage to having more fans, but their contribution to ROI is relatively small. Making decisions that focus on increasing the number of fans likely wastes more money than warranted by the ROI you’ll see.
- Measure too many metrics and analysis becomes difficult. It’s like trying to find a needle in a haystack. The vast amount of hay (useless data) hides the useful data — the needle. Just because you CAN measure something, doesn’t mean you SHOULD.
- Accumulating metrics costs money. Data is only worth its cost when the value it contributes to improved decision-making outweighs the cost of collecting the data — and be sure to include the human cost of gathering and analyzing this data to other costs in acquiring the data.
Step 2. Gather relevant data.
Some data comes from internal sources, such as sales records, accounting figures, website analytics, and reports from your sales force. Other data comes from external sources, including competitor results, economic metrics, listening post metrics, and Facebook Insights. Your marketing research data can also form part of your marketing information system, such as recurring surveys of customer satisfaction.
Step 3. Plot data.
It’s very difficult to make decisions based on raw data because the data contains invisible patterns that might otherwise indicate appropriate actions. For instance, it’s hard to see a downward trend in customer satisfaction without graphing the data until the decline is substantial and by then it might be too late to reverse the trend. Plotting data allows managers to quickly detect changes in critical metrics over time. Sure, you can use the graphing function of Excel or other data program, but newer data visualization software makes the job even easier. Examples of software providing superior data visualization are: IBM Cognos Insight, Tableau Software, and SAP Visual Intelligence. Some even create dashboards to bring all your marketing metrics to one place, making decision-making even easier.
Step 4. Communicate results.
The more people who have the information from your marketing information system, the better. But, not everyone is going to understand tables of raw data or even visualizations like in the dashboard above. That’s because your marketing information system requires interpretation through the lens of marketing knowledge.
An example is my hierarchy of effect model of social media marketing. Not only does the model suggest appropriate data to collect, such as sentiment, #likes, # comments, #shares, size of engaged audience, and participation in customer support, the model suggests how these metrics translate into ROI from your social media efforts. Because readers might not know of or understand this model, data interpretation would include
references as to WHY metrics are important for reaching firm goals.
Step 5. Make marketing decisions.
The final step in the process is using metrics from your marketing information system to make decisions that optimize your marketing outcomes.
As an example, metrics from your social media campaigns show how successful you are in driving visitors down the hierarchy of effects. You should produce more posts that successfully move visitors down the hierarchy — ie. produce more engagement. Metrics might also indicate optimal times to post, # of posts per day, and other useful information you use to guide future posts. On my own Facebook fan page I find images get more engagement — sharing, liking, commenting — than other types of posts, including posts containing a link and an image. Thus, I’m working to create more image-based messages, such as images containing links to my website or to subscribe to my email list.
Do you have a marketing information system? What have you learned since adopting a marketing information system?
At Hausman and Associates, we’d love the opportunity to show you how our metrics based marketing and social media programs can make your marketing SIZZLE.