The author of the article: Elena Berezovskaya, head of the web analytics department at the agency
Elena Berezovskaya, head of the web analytics department at the agency
Data analysis for business and marketing has been a trend for years. But the myth that analytics is expensive, challenging, and in general, the lot of large companies; is still widespread and stops the implementation of this helpful tool.
Today, together with Elena Berezovskaya, head of the analytics department, we will try to dispel all myths and tell you why a business needs analytics and how to implement it.
Why does a business need analytics?
The main goal of the implementation of analytics is to increase profits.
This goal forms a series of challenges in which data plays a key role:
Increasing the conversion of visitors to customers – in other words, increasing interaction with the target audience, those people who need your product;
Increasing the bill allows the company to increase its income with the same volume of buyers.
Assessing the role of marketing communications – allows you to optimize costs by reallocating the budget to channels that affect sales.
Analyzing target audience behavior – customers’ taste preferences change under the influence of internal and external factors such as fashion, economics, technological development, etc. By tracking changes in behavior, we can make an effort to retain and attract customers.
The retention of existing customers. In niches where re-sale is possible, in most cases, regular customers generate 80% of the income; Accordingly, by retaining such users, the business gets a lot of quality sales at a minimal cost.
Tracking market and competitor trends – if you miss this information, the effectiveness of advertising campaigns will be difficult to assess correctly; For example, in a situation where advertising campaigns did not bring sales growth comparing the previous year. However, if the market has significantly decreased, holding last year’s positions is already a growth.
Reduced attraction costs – if we spend less on the attraction of one sale, we get growth within the same budget;
The attraction of the new customers – we mentioned above that the primary income comes from repeat customers. So why should we spend money on new and expansive customers? The fact is that sooner or later, current customers may leave (someone will move, someone will change their taste, and someone will move to competitors). So, if you do not attract new ones, then at some point, the permanent audience will cease to generate income.
Evaluation of business processes. It is not enough to attract a user to a site or store; it is also essential to provide quality service and product. Without it, any marketing investment would be a waste of money. Therefore, it is crucial to track at what stages the failures occur and for what reasons.
These tasks are only a small part of what a business can use analytics for.
How a business can start implementing analytics
First of all, you need to remember that the implementation of analytics is a separate project, for building of which we should take into account the following factors:
budget – the implementation and use of analytics requires the cost of specialists and the payment of some services;
implementation time – depending on the complexity of the analytics, setting up the collection and starting to use the data can take on average from a month to six months;
labor resources – to create analytics, time is needed not only for the specialists who will be engaged in the configuration but also for those who will use it;
In order for the system to collect the necessary information that we can trust, attention and participation of all key employees are required, and, first of all, those who will eventually use it, for example, a director, a marketer, a general sales manager, etc.
Payback – since analytics is aimed at increasing profits, its implementation must necessarily pay off. Yes, tracking the ROI of analytics is hard since these are indirect indicators, for example:
– If the goal is set correctly, advertising campaigns can bring 20% more profit.
– When creating a report on the effectiveness of products in their promotion, you can focus on high-margin products or make up-sales.
– By analyzing the reports on the effectiveness of managers, you can find weaknesses in the sales funnel, and getting rid of these weaknesses can increase the percentage of sales;
When implementing analytics, we recommend using the adequacy rule (the rule that the ADINDEX team came up with):
Pay attention to the main question: “How will I use the information?” If you do not know why to collect information or implement technology, then, with a high degree of probability, you will lose money;
Let’s see the rule of adequacy in action. Let’s say you are planning to implement call tracking. What do you need for this?
Step 1. Answer questions
How will I use the information received?
- Optimize AC (advertising campaigns) by calls.
- Track the sales funnel (channel – call – sale) and manage advertising channels.
- Analyze the performance of individual sales managers and improve lead handling processes.
What information do you need for this:
- The event in Google Analytics about incoming calls from the site;
- Lead status and time of its arrival and processing;
- Manager’s name;
- Parameter for combining Google Analytics data, call tracking, and transaction (this could be, for example, ClientID, call ID, etc.);
How much will I earn from using call tracking?
It is planned to increase the number of requests by 15%, and the conversion rate of a lead to sale will increase by 5%. As a result, implementation of the system will pay off in two months, and the sales volume will increase by 7%.
We conclude that it makes sense to implement call tracking.
Step 2. Technical implementation
Next comes the implementation phase.
Codes are setting in, events and targets are setting, dashboards are creating – this is the easiest part since you hire specialists to customize it. Next, you need to rebuild processes for the existing data.
The figure shows the data movement diagram. As a result of the setting, we get:
- Data for optimization of advertising campaigns per call;
- Information panel “Analysis of the effectiveness of the sales department”;
- Information panel “Analysis of the effectiveness of the AC”;
Step 3. Using the system
The next step is the most difficult, and it is associated with implementation, with the beginning of using the information received. For our example, you need:
- to notify the PPC specialist that there is a goal in Google Analytics that displays real calls and that it should be used to optimize advertising campaigns;
Provide information on channel performance.
- Train the head of the sales department to use the dashboard on the effectiveness of the sales department, to show which questions you can see the answers to on this panel;
- Train the marketer to use the information panel about the effectiveness of advertising campaigns.
As a result, from setting up to full implementation and use of the analytics system, it is necessary to make a lot of effort on the part of those people for whom it is created.
As you know, at a subconscious level, people are repelled by everything new. And that’s okay. Therefore, we have prepared a short checklist on how to help the team implement a new process.
After you have made sure that the system is using correctly, you can proceed to the final stage.
Step 4. Final implementation. Outcome
After the implementation is complete, analyze and compare the expected and actual results.
If the actual result is as expected or better, congratulations, you have successfully implemented the system!
If the result is worse than you planned, then the following questions will help you understand the reason:
- Are you using the implemented system as planned, or did you only partially implement it?
- Has the market situation changed during the implementation?
- Has the marketing strategy changed during the implementation period?
- What changes have occurred among employees (perhaps your team has changed)?
Using the example of call tracking, we showed how to make decisions about the implementation of analytics.
To summarize all of the above, remember:
- Analytics is a full-fledged project; it should pay off.
- Using data for applications is the most efficient and challenging part of implementing analytics; without it, everything is meaningless;
- Implement analytics gradually – from simple to complex; otherwise, you risk creating a complex, clumsy system that will gather dust somewhere in the depths of servers and cloud storage.