How to look at marketing data to solve a problem

How to look at marketing data to solve a problem without getting stuck on “innocent” results that waste time? The article below is part of a series of articles about data-driven marketing. Let us talk about it with Weapon Agency!


  1. Key performance indicators first, then go into marketing data.

The success of a campaign may be evaluated in various ways, with different metrics being used for each iteration. However, not every statistic in performance marketing is a KPI (key performance indicator). For example, reach, frequency, and engagement data may not be beneficial if, for instance, sales are the firm’s primary focus. On the other hand, sales-related indicators such as cost per lead, lead, and lifetime value will offer more realistic insights.


Setting KPIs in line with company objectives and then modifying the reporting structure on a weekly/monthly/quarterly basis to reflect these KPIs is an excellent place to start before diving into marketing data analysis. However, if you do not sort through the “mess” in the data supplied, you will wind up “a little bit here, a little bit there,” which is a dead end.


  1. Determine the meaning of each indicator.

It is essential to understand the context of each indication to help marketers explain the performance of the ‘marketing and sales funnel’ and localize the problem in each stage. Some examples of these measures include the click-through rate (CTR), the bounce rate (the percentage of visitors who leave a website without taking any action), and the conversion rate (the percentage of visitors who make a purchase).


Directly implementing advertising/advertising activities? Then you are probably already aware of the significance of the above three metrics:


CTR: The proportion of clicks shows how “appealing” the media messages are to the intended audience. If your click-through rate is too low, you may want to upgrade your reach (for example, changing: text and, images, colors)


Bounce rate: A good return on investment is possible with a low bounce rate and a high conversion rate (about 1-3%). Instead, it would help if you looked at the customer’s whole experience to identify the weak spot. 

For example, the web might be too clumsy, the information could be irrelevant, or the company could have set “false expectations” from the beginning of the message but then failed to “deliver as promised” on the landing page/website.


  1. Analyze only relevant data with the ‘original question.’

Not every insight can become a ‘conversion’ for an advertising campaign. Start with the goal in mind when analyzing data. What are you trying to prove? For example, if you see a sudden increase in the effectiveness of your marketing for a few weeks before it goes back to normal, start by asking “why?” and find the data that will help you figure out what is going on.


Once you know your goals and the data you need to work with, you can sometimes set up automated formulas to make it easy to keep track. If not, the source of data that grows every day will make it harder and harder to get data from scratch, especially since many business data is not “tracking” from scratch.


  1. Focus on the big picture

Create a comprehensive picture by connecting many “data sources.” For instance, while conducting customer data analysis, it is not easy to get a clear picture of your client base from the report data provided by Facebook, AdWords, Google Analytics, or email marketing software. Why? Because like the ‘elephant fortuneteller”. A’ fracture’ lurks around the corner to cast doubt on our predictions.


By contrast, organizations will have a fuller picture if data can be connected across channels, departments, and divisions using tools like customer relationship management software.


  • Priorities are then set based on the likelihood of each object’s conversion.
  • Groups of people might buy your goods or use your service, for example, those interested in inexpensive but high-quality options.
  • Adaptations in behavior or the emergence of new, unfulfilled wants
  • Possibilities to get more excellent value from existing clientele.


  1. Compare results and goals

Making ‘arbitrary’ changes to advertising campaigns is the biggest no-no!

Having a target in mind while testing or making adjustments is important. When optimizing for a single objective, such as “grow leads by 10%” or “lower cost per lead to X thousand,” the consequences of changes to content, targeting, budget, keyword group, and run duration will alter positively or negatively.


Regardless of the outcome, each modification will provide informative information in the month-end report. So, spend a little time learning how to do A/B testing so you can rapidly explain the impact of each strategy or adjustment to the final figure!


  1. Connect data online – offline if possible

Building a target customer profile based on digital behavioral data is good, but it will be even better if businesses can connect online – offline data about their interactions with the brand. For example, by applying CRM to operational processes to create a ‘common interface’ for sales and marketing departments to share data, marketers will have more insight and overview to:


Optimizing the approach for each target audience

Personalize care content for each target group

Shorten the process of care and transformation

So when analyzing marketing data, if you have reports and offline research, don’t miss this ‘potential gold mine!


Building a target consumer profile based on digital behavioral data is fine, but it would be great if firms could combine online-offline data regarding brand interactions. By adding CRM to operational operations to provide a ‘single interface’ for sales and marketing to share data, marketers will have a better understanding of:

  • Optimizing the approach for each target audience
  • Customize care materials for each intended audience
  • Shorten the process of care and transformation

Do not pass over this ‘possible gold mine’ while studying the marketing data, especially if you have reports and offline research.


  1. Analyze website data at the ‘page’ level

If you employ SEO and inbound marketing to drive visitors to your content, it would be a waste if they do not read it. Thus, companies will instantly discover what content needs improvement or how to route material so that the Target audience is guided to pages that are likely to convert while doing a comprehensive analysis of the interaction behavior of the target audience in Google Analytics, for example, inside content.’


Therefore, when deeply analyzing the interaction behavior of the target audience in Google Analytics – for example, in “site content” – businesses will instantly recognize what content requires improvement or how to navigate customer journey so that the Target audience is directed to pages that are likely to convert.


  1. Identify key customer ‘sources’

Data analysis for marketing purposes should follow the visitors from their initial visit through their submission of information (lead) to their eventual purchase (customer). You can learn much about the customer journey and where purchases are made by monitoring their digital footprints.


Nonetheless, do not stop there; ponder things even more profoundly, like in


  • How to boost the number of leads in the channel that brings in the most customers if that channel does not bring in the most leads?


  • You should identify the issue if the most effective lead generation methods are not ‘converting’ their visitors into paying clients.


Businesses may need to improve ‘lead quality’ by adjusting some factors.


‘Change in quantity to a certain degree will cause changes in quality,’ so sometimes it is best for firms to focus on getting as many leads as possible.


Recently, some guidance, broken up into multiple parts, has been presented. The paper is not well-structured, but it may be slightly helpful and lead to more methodical approaches to analyzing marketing data. In addition, the Digit Matter blog is constantly updating with fresh articles to help you “dig deeper” into data-driven marketing and analytics.

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