How Offline Conversion Data Can Make Your ROMI Calculations More Precise

Do you calculate ROMI properly? Don’t worry, this post is not about listing different formulas, though there are some. It is about deeper understanding of this indicator's nature. Every self-respecting marketer estimates the efficiency of their investments by calculating their ROMI (return on marketing investment). The formula for doing so is so simple that this marketer can repeat it by heart, even in the dead of night. Here incremental revenue attributable to marketing can be calculated in three steps:

  1. Add up your total revenues for a period of time, such as a month, a financial quarter or a year.
  2. Calculate your total revenue after introducing the new marketing campaign.
  3. Subtract your base revenue (step 1) from your new revenue (step 2) to calculate your incremental revenue.

A product’s contribution margin is its price minus all associated variable costs, which will give you your incremental profit earned for each unit sold. Your marketing spend is your total expenditure on marketing activities. Let’s look at an example. Assume that you had a total revenue of $80,000 for the previous month. This month, you introduced a new marketing campaign, and your total revenue increased to $110,000.Your incremental revenue here is $30,000. Now let’s imagine that you’ve been selling goods or services for$350 per unit or service, which includes a variable cost of $245. That means your contribution margin is 30 percent.

This result proves that your marketing expenditures are paying off, and that every dollar you spend on direct advertising earns you an additional $0.29. Naturally, there are other formulas for calculating ROMI, but the principle is the same. But, is this type of calculation enough to make data-driven marketing decisions? The obvious answer is no. When calculating your business’ general ROMI, you can estimate your overall efficiency, but you still won’t know what your main weak and growth points are. To draw a parallel, imagine a medical examination in which a doctor concludes that a patient is healthy just because he’s alive! An undiagnosed condition can do a great deal of harm. Similarly, in business, one failed campaign can lead to significant profit loss. That’s why it’s not enough to assess the efficiency of a marketing mechanism as a whole. You have to know the efficiency of each one of its elements separately.

How to make your ROMI calculation more precise

You can and must narrow the domain of ROMI calculation. When you know the ROMI for each channel of your marketing campaign, you’ll understand exactly how every cog in your marketing mechanism works. It’s best to calculate the ROMI for each of your advertising campaigns, or even more granularly. The first thing you need to do is to determine whether the data you have on a specific campaign is enough to work with. Depending on its statistical significance, you can calculatethe ROMI for each advertising campaign, for each group of ads, or even for each search query that leads to your website. Next, you can present your results as an array of ROMI figures for each of your search queries calculated for different periods. Such data visualization will provide you with valuable insights into your marketing performance. But what if you don’t have enough information to analyze your ROMI? You can merge data on different queries according to particular rules, which will allow you to create a new cluster of queries that can be analyzed as a separate unit. This will be possible because each cluster of queries will have something in common. The  easiest way to create clusters is to merge all search queries that belong to a particular advertising campaign (as mentioned above). This will show you whether or not your campaign is paying off and how much profit is being generated by each dollar invested in it. Moreover, imagine this situation: a few major web-analytics systems (these could be Google Analytics, Kissmetrics, Woopra, etc.) show that a cluster of specific requests is performing well and generating a lot of conversions. Naturally, marketers will consider this cluster to be more efficient and keep investing in it. But calculating the ROMI for this cluster could show that these queries aren’t generating profit. However, there’re a lot of other ways to approach clusterization, and it’s up to you to determine which will work best. Here are some examples:

    1. Semantics.. Imagine that you own an online store that sells electronics and accessories. You can merge all users’ search queries that contain the word “phone cover,” for example. This might include “buy a phone cover,” “phone covers for Samsung Galaxy S7” or “orange phone covers.” Similarly, you can also merge all requests that contain the word “headphones,” for example, “buy headphones,” “cheap headphones,” etc. By doing so, you’ll be able to calculate your ROMI for each type of goods that your online store sells. Another example: you can merge all top-category goods (i.e., covers, headphones, smartphones, etc.) into one cluster.2.
    2. Type of request.Search queries like “buy Samsung Galaxy S7” or “buy a smartphone” are called transactional, which means that users who type these requests intend to make a purchase. At the same time, queries like “IPhone 6S specs” or “Samsung Galaxy S7 video review” are informational. If a query is short, like “IPhone 6S,” it’s difficult to guess the user’s intention, but this query can also be analyzed separately.
    3. You can easily find other examples of clusterization and select the one that will work best for your business.

Another important part of calculating your ROMI is using a CRM system. This may seem strange, however, because marketers typically get enough information from different web analytics services and don’t need additional tools. At least, they think so. Imagine that 500 users decided to buy item X and placed an order. This data went into a web analytics system. Item X turned out to have defects, and 100 customers returned it. The company’s profits decreased, but the data in their web analytics system didn’t change. This means that the marketers received a ROMI value that was incorrect. In order to prevent such a situation, you should use a CRM system. It will send the updated information on such deals to web analytic systems. Such an approach to analytics is certainly more precise and efficient. However, could something still be missing?

Filling gaps in analytics

So what about offline conversions? As you know, filling out an online form isn’t the only way to make a purchase. Lots of people still prefer to order by phone when shopping online. If a number of offline conversions makes up less than 15 percent of the total conversions you receive, you can neglect it. Otherwise, these conversions need to be taken into account too. If a company has a large percentage of offline conversions and doesn’t analyze them, its analytics reports are not 100 percent reliable and will show incomplete data. Moreover, behavioral trends of customers who call and who don’t, are different. They represent two separate groups of people, and you need to approach each one differently. The above-mentioned clusterization can also be useful here. So, the main point is: take into consideration both online and offline conversions when calculating your ROMI for different campaigns and clusters.

How to incorporate offline conversion

With the development of call tracking services (such as Ringostat call tracking), offline conversions are no longer a blind spot for marketers. Because these services can transfer data on phone calls to different CRM systems, you can analyze phone calls together with other types of leads. Here’s how it works. Once phone calls are logged in a phone analytics system, they are transferred to a CRM and a web analytics tool. This happens in two steps:

  1. A call tracking service aliases a client’s phone number to a Client ID– a unique identifier assigned to a user by a CRM system.
  2. The service then transfers data on the phone calls to a CRM.

Most CRM system allows you to clusterize data based on received information about calls and their sources. At this stage, you can calculate your ROMI for each cluster separately and draw conclusions on its efficiency. Next, you can transfer the data from your CRM to Kissmetrics and get a full picture of your marketing efforts. Here’s how you can get a clear and reliable picture of your marketing campaign’s performance:

  • Use a call tracking service for offline conversions
  • CRM system to adjust the information about your online and offline conversions and merge different data segments
  • Use Kissmetrics for online conversions. It will also process information about your online and offline conversions and present it in a flexible, customizable way. Thus, Kissmetrics becomes a universal tool you can use for analytics.

To sum up, there are several things you should consider when calculating ROMI. First of all, make the domain of its calculation as narrow, as possible. If not having enough data, clasterize it. Then think about including your CRM system in the list of must-have information sources. Finally, don’t forget about offline conversions, as they can significantly affect ROMI value.

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