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6 Easy Steps to Generate New Revenue With Marketing Analytics

By Ateeq Ahmad | September 17, 2018

In the days of old, traditional marketing campaigns would rely on print, radio and TV to advertise their products or services. The only way to measure campaign success was to making indirect correlations with revenue (i.e. if sales went up post campaign).

 

In recent years there has been an explosion of marketing tools available. Marketing leaders now have the ability to track all aspects of their campaigns (and the flexibility to spend on any channel that generates the most ROI).

 

Marketing can now become a profit center as opposed to being a revenue drain.

 

But this boom in marketing analytics has generated tremendous confusion around the field. Experts are perplexed by which tools and platforms to use, what data to collect, and of course, how to increase the bottom line?

 

This guide will help set you on the right course. It will list out some of the crucial elements required to set up your marketing analytics infrastructure.

 

Strategic KPI’s

 

strategic KPIs

 

The best place to begin is with your goals and KPIs. It becomes hard to determine what tools you should be using if you are not focusing on KPI’s. Every industry has different tactical KPI’s but almost always you will need to focus on the following at a strategic level:

 

CPM (cost per thousand impressions): Impressions (or pageviews) refer to the number of times an end user views your marketing message (this could be a page on your website, an ad copy on the web, a landing page, a billboard, etc). CPM refers to the cost to receive one thousand impressions. Mathematically, CPM is derived using the below formula:

 

CPM = [Spend/Impressions] x 1000

 

CTR (click-through-rate): Clicks refer to the number of times an end user sees your marketing message and clicks on it (i.e. a click on a banner advertisement, textual ad copy, social media post, etc). This yields the rate at which end users click on your marketing message. Mathematically, CTR is derived using the below formula:

 

CTR = [Clicks/Impressions] x 100

 

CPC (cost-per-click): As the name implies, this metric defines the cost to receive one click. This metric is pulled by taking the total spend (or cost) of a campaign and dividing that number by the number of clicks. Mathematically, CPC is derived using the below formula:

 

CPC = Spend/Clicks

 

CPA (cost-per-action): An action is when an end user completes a goal as a result of their marketing experience (i.e. fill out your form, call sales, or even share a post on the web – it depends on what your goals are). CPA refers to the cost to receive one action.  Mathematically, CPA is derived using the below formula:

 

CPA = Spend/Actions

 

Conversion Rate: Is the rate at which end users complete a goal (i.e. action rate). Mathematically, Conversion Rate is derived using the below formula:

 

Conversion Rate = [Actions/Clicks or Website Pageviews] x 100

 

Leads: These are end users that have expressed an interest in your product or service as a result of a marketing campaign (i.e. by filling a form on your website, or calling sales). Lead records populate on the Leads object of a CRM (like Salesforce).

 

Marketing Qualified Leads (or MQLs): These are leads that have been further qualified by sales development reps (or SDRs), usually by a phone call to verify their interest. MQL records typically populate the Opportunities object (the SDR will convert them from Lead to Marketing Generated Opportunity).

 

Sales Accepted Leads (or SALs): If the MQL passes the SDR’s verification, it is routed to the correct Account Executive (or AE) that will continue the sales follow up efforts (until the deal is either won or lost). If the AE finds the MQL to be viable they will accept it, and it will convert from an MQL to an SAL. This is usually denoted by a value on a field on the Opportunity object.

 

Sales Qualified Leads (or SQLs): As the AE further qualifies the SAL, talks progress to where dollar amounts for services or products are being discussed. When the prospect agrees to a non-zero currency amount, the SAL converts to an SQL (this is denoted by a non-zero pipeline bookings value on the Marketing Generated Opportunity record).

 

Marketing Generated Opportunity (or MGO): In accordance with a set attribution model, is any opportunity that is generated by a marketing effort (it does not necessarily have to follow the above path of Lead>MQL>SAL>SQL). These opportunity records reside on the Opportunities object of a CRM (like Salesforce).

 

Sales Generated Opportunity: In accordance with a set attribution model, is any opportunity that is generated by sales’ efforts (and not marketing efforts). These opportunity records reside on the Opportunities object of a CRM (like Salesforce).

 

Pipeline Bookings: Currency value given to any opportunity that is still open.

 

Won Deals: A won deal refers to any prospect that has bought your product or service as a result of your sales funnel (this could be marketing or sales generated).

 

Revenue: This is the currency value given to any opportunity that converted to a Won Deal.

 

Cost-per-Acquisition (or CPA): The cost to acquire a customer (or Won Deal). There are different ways to segment CPA data (such as by source, channel, offer type, sales rep, etc). This allows you to drilldown to see which marketing efforts are most effective (and should be scaled), and which need to be tweaked (or even scaled down for the interim). Mathematically, CPA is derived using the following formula:

 

CPA = Spend/Won Deals

 

Marketing Generated Revenue (MGR): As the name implies, this metric refers to the Revenue generated by Marketing Generated Opportunities. Marketing exists to increase revenue, so this metric will make a powerful case for further budget approvals.

 

Return on Investment (ROI): This is the “end-all, be-all” metric. If you have spend and revenue, then calculating ROI becomes easy. Here’s the formula:

 

ROI = [Revenue/(Spend] x 100

 

This yields a percentage value that should always be a positive number. Segmenting ROI by dimensions like channel or source greatly increase visibility into what’s working and not working in marketing.

 

Customer Lifetime Value (LTV): This metric shows the projected value a customer is worth over the lifetime of the relationship. By uncovering your most profitable customers, you can take steps to channel your resources more effectively for higher profits in the future.

 

Data Collection

 

data collection

 

The next step is setting up your data collection platforms. This could be Google Analytics, Adobe Analytics, marketing automation systems like Hubspot, CRM systems like SalesForce, or even ad platforms like Facebook Analytics.

 

Keep in mind that as it stands, these platforms operate in separate silos that usually will contain discrepancies in data.

 

While on front-end platforms this is okay, for backend platforms (like marketing automation or CRM) it’s important to sync those systems and merge as much of the data together as possible (more on this below). Then stick to a single source of the truth.

 

This may seem like a lot of work but if you outsource the legwork to a reputable agency it may relieve much of the headache that comes with setting things up.

 

Set Your Attribution Model

 

set your attribution model

 

This is where you define how each marketing effort gets credit (or attribution) for driving website traffic, conversions, and thus sales and revenue.

 

There are five main types of attribution models that are used. They are as follows:

 

  1. Last click: You give all the credit for the conversion to the last-clicked ad or organic keyword.
  2. First click: You give all credit for the conversion to the first-clicked ad and the corresponding keyword.
  3. Linear: You distribute the credit for the conversion equally across all sources that contributed to the conversion. For example,  if the user path was Organic, paid, email and direct; then each method will get 25% credit.
  4. Time decay: You give more credit to clicks that happened closer in time to the conversion.
  5. Position-based: You give 40% of credit to both the first- and last-clicked ads and corresponding keyword, with the remaining 20% spread out across the other clicks on the path.

 

If you have a CRM system you will need to set an attribution model separately there. The first touch model works best here whereby all credit goes to the first source/campaign that generated a lead or opportunity.

 

You would also need to avoid duplication in this scenario by using timeout windows in conjunction with filters for prospects and associated products/services.

 

Merging Data Sources

 

merging datasources

 

At some point the enterprise slows down because there is too much data, but no unit can understand the other’s language. Between the varying teams, confusion arising from a lack of a “single source of truth” (or SSOT) can be quite debilitating in terms of decision making.

 

At this point you can start to go down two different pathways. You can either build your own data warehouse or buy a tool that will act like one for you.

 

Building a data warehouse comes with the advantage of designing your own data collection and downstream KPI’s that can be customized as desired. There is a high initial cost but then the flexibility of operation tends to result in a strong return.

 

Having a data warehouse means quick deployment to any changes to your analytical infrastructure. It gives more flexibility in how data is aggregated and presented as well.

 

The data warehouse can be used with any of the popular data visualization tools like Tableau, Power BI, Qlik or Google Data Studio.

 

Analysis

 

analysis

 

Once you have all the data it’s time to start mining insights. You need to have a core set of people who are going to make sense of it all and provide recommendations.

 

Generally speaking, many organizations build dashboards but are unable to extract value from them because they are too complicated to interpret.

 

This is where your trusted analytics person comes in. They will look at all of the charts, graphs, and other visualizations and start identifying opportunity areas with specific suggestions for improvement. They will also help liaison the communication between the marketing team and upper management.

 

Big Data & Predictive Analytics

 

predictive analytics & big data

 

Once your analytics infrastructure is in full swing start thinking about using that data in as many different ways as possible.

 

A one-size model in terms of pricing, deals or discounts does not remain a good strategy for long. Very soon your marketing team will run into a wall where they keep acquiring new customers or re-acquiring current customers without enough understanding of their buying behavior.

 

At this point look for tools that segment the population into meaningful chunks and target them based on their needs at a particular time.

 

There are several open source languages like R or statistical packages like SAS and Tableau that can help your internal analytics team do this work for the business. These require the in-house team to have a strong data science background.

 

This path will give you the best ROI because the tools will be made in-house and can be customized based on the business.

 

Conclusion

 

If you have executed on the above mentioned items you will already be ahead of the competition and most certainly will begin to see growth in marketing generated revenue.

 

As always, if you are having trouble implementing any aspect of your marketing analytics infrastructure, please do not hesitate to reach out to us. We will make set up a breeze!

 

 

Have you already gone through this process of setting up your marketing infrastructure? If so, tell us your story below in the comments section!

 

Ateeq Ahmad

Ateeq Ahmad

Ateeq Ahmad writes articles on big data and analytics for Xoobo. He has experience managing key projects and bringing new analytics products and services to market. For the last two decades, he has been a respected leader and thinker for e-commerce websites and digital tools. He is a technology and product leader with experience in managing very large teams and his career spans various industries and countries.

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