How to Use Agile Analytics to Quickly Solve Your Conversion Problems

Last Updated: January 21, 2023

‘Agile’ is the ability to move quickly and easily.

In analytics, agility is the ability of an analyst/analysis to adapt rapidly and cost efficiently in response to changes in the marketing environment.

The aim here is to conduct a very focused analysis, quickly deliver recommendations, and rapidly deploy solutions to solve conversion problems.

In Agile Analytics, the success doesn’t come from the level of insight you get but from your ability to rapidly deploy solutions which solve your customers’ problems either wholly or in parts.

We live in a world of constantly changing marketing conditions and customer needs.

We all know how fast the search engine and social media landscape is changing. Almost every hour something new is being developed/introduced and at the same time something old is being discarded or labelled ineffective.

In order to respond fast to these changes, capitalize on new marketing opportunities, and make super timely decisions you need to adopt agile analytics methodologies. In agile analytics you don’t spend 3-4 weeks in gaining insight and then delivering recommendations in a lump sum at the end of each month which is usually based on 1 or more months old data.

In an agile environment, you deliver solutions weekly if not daily.

Like all things agile, the agile analytics focus primarily on “customers”. How quickly and how often you can create value for your customers determines how agile you are in your methodologies and in your mind.

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The following tips can help you greatly in adopting agile analytics methodologies and quickly improving the business bottomline:

1. Solve for your customers and not for KPIs

We often hear goals like “we want to improve conversion rate by X” or “we want to increase traffic by Y” from marketers and business owners. While nothing really is wrong with these goals, they are simply not agile. You can’t rapidly deploy solutions with such goals.

For example:

Say you want to improve the conversion rate of your website. Now you have no idea how to do that without some deep data analysis. You are not sure where to start, which report(s) to look, which report(s) to overlook.

So you start your analysis. You go through reports after reports looking for anomalies and to find something which may need fixing. You have no idea where your analysis is going to take you and how long it is going to take.

But you have to find a solution, so you segment and analyze the data. You repeat this process over and over again until you find something which needs fixing. This process is very time consuming and certainly won’t lead you to make timely decisions.

Now try to solve the same conversion problem the other way,

Instead of focusing on your KPI, focus on solving your customers’ problems one at a time.

Say through customers’ feedback, you found out that there is some technical problem in your shopping cart which is causing your customers to abandon the cart.

Now you know the problem and all you have to do is to go ahead and recommend fixing this problem ASAP. Once you fix your customers’ problem, your conversion rate is going to increase anyways whether or not you structure your entire work around improving this metric.

You could have found and fixed the same problem by focusing on your KPI too. But it would have taken you much longer to find and fix the same problem.

This is the power of agile analytics. You quickly find problems and rapidly deploy solutions.

2. Make VOC your daily habit

VOC stands for ‘Voice of Customers analysis‘. VOC is the process of determining customers’ expectations, preferences, and objections.

You can conduct VOC through:

  • Online and offline surveys

  • Market/industry research reports

  • By conducting A/B tests

  • By analyzing website usage (what customers do on your website)

  • By interviewing your sales and customer support staff

  • Through social media monitoring

However in order to respond fast to the ever-changing needs and expectations of your customers and to constantly innovate, improve customer satisfaction and increase customer lifetime value you to need conduct VOC all the time, day in, day out.

We often see marketers conducting surveys, once in a while and that too only when they are not able to find a specific problem/solution through quantitative analysis. But

in an agile environment, we run surveys all the time. We continuously collect customers feedback and act on them in a timely manner.

This is the reason why I was able to find out quickly through VOC that there is some technical problem in my shopping cart that needs fixing. Even in the future if some other technical problem popped out with my shopping cart system, I know my customers will alert me.

In a non-agile work environment, I would have run the same survey only after I wasn’t able to find any problem to fix through my quantitative analysis, thus wasting my time and resources in first collecting and analyzing customer data for a month or so before I could take any action.

If you continuously gather customers feedback, you would never really be short of finding the problems that need fixing. Trust me on that.

You don’t need to rely on your own understanding of the client’s business or on the client himself to figure out what problems need fixing and what needs to be improved.

Every website has got 10000 problems that need fixing. But nobody has time and resources to fix them all.

In order to prioritize your tasks and manage your resources, you need to collect customers feedback data all the time, 24 hours a day, 7 days a week.

Your customers’ feedback will almost always highlight the most important issues that need fixing first. Without such feedback data, you may end up spending a significant amount of time and resources in finding and fixing a problem that doesn’t really matter to your customers.

If something doesn’t matter to your customers then it won’t help you in improving the business bottomline. It is as simple as that.

Thus agile analytics also saves your time and resources. Listening to your customers will almost always help you in getting one step closer to becoming a market leader.

This is the power of agile analytics. You spend less time finding problems and more time fixing them.

3. Become omniscient and be omnipresent

Omniscient is a person who knows everything and omnipresent means being present everywhere. Of course, I am not saying to become omniscient or be omnipresent literally. Only God can be omniscient and be omnipresent.

In the context of agile analytics, becoming omniscient means being aware of what is going around in your organization.

You as an analyst must be aware of every activity, news, event, and changes that can/is significantly affecting your data.

These changes can be something like:

  1. Major site redesign

  2. Launch of a new product or a promotional campaign

  3. Discontinuation of a product, process or campaign

  4. A significant change in management, company policies, marketing budgets or processes

  5. Any change to your Google Analytics account (like adding or removing filters, adding or deleting views, etc)

  6. Any change to the way data is presently collected, integrated and analyzed

  7. A considerable change in the consumers’ behavior

  8. A considerable change in the competitive landscape (like the entry of a big and powerful competitor)

  9. Any positive or negative news about your company and competitors.

  10. Change in the economy, market conditions, etc all of which can affect your data.

Being omnipresent means being present everywhere where you should be in your organization.

Which means you need to be present in every board meeting, every marketing team meeting and should be CC’d or BCCd in every important email in which major decisions are being made about your website(s), promotional campaigns, processes, marketing budgets, the way data is presently collected and integrated and the company itself.

By being omniscient and omnipresent you will greatly increase your ability to conduct very focused analysis from the very start. It will save a lot of your time and resources. For example:

Say one day in the morning, you found out that the website traffic has decreased by more than 50% in the last one week. At this point you are not sure why. So you start your analysis.

You go through reports after reports looking for anomalies and to find something which may be broken. Again you have no idea where your analysis is going to take you and how long it is going to take.

Let say after few hours, you determined that a traffic from a particular campaign decreased by more than 70% in the last few days. You then called the person who is managing the campaign. He told you that he stopped the campaign for few hours because of some site maintenance work.

Had you know in advance that the campaign will be paused for few hours, you would have made a note of it and didn’t have to go through all this trouble of finding out this information on your own.Your time could have been better spent doing some really meaningful analysis.

This is a very common scenario. It happens all the time. Often we waste time and resources digging out information that is already known to someone somewhere in our organization.

So all you have to do, to fix this problem is to be in the loop.

That is why you need to build a strong relationship with your marketing team, you need to build a strong relationship with all those people who can help you in explaining any anomaly in your data.

4. Maintain a database of all the changes that significantly affect your data

In order to conduct a very focused and meaningful analysis you need to maintain records of all the changes that significantly affect your data every single day.

These records will help you greatly in interpreting the various spikes in your data trends even months from today. You would no longer need to remember what event triggered an anomaly and when. Everything is stored at one centralized location. This centralized location can be a spreadsheet or full-fledged database application.

I suggest you read the Google Analytics and Google Adwords change history every day. Set up Google Analytics Intelligence Alerts.

Intelligence alert is a feature in Google Analytics which monitors your website traffic to detect significant changes and creates an alert when something important happens on your website.

It can help you get insight which might otherwise get unnoticed. 

Note down anything which seems important like considerable change in the marketing budget of a campaign, addition of new filters, etc.

Attend every board meeting, every marketing team meeting, or any other stakeholder meeting. Note down all the changes that may significantly affect your data like changes in the marketing budget or changes in the way data is collected or integrated.

Be very aware of what is going around you, on the website, processes, budget allocation, and even internal politics. Note down anything which you think may affect your data.

5. Solve problems along the way

In agile analytics the main focus is on deploying solutions as quickly as possible. If you are omniscient and omnipresent, if you are maintaining a database of all important changes, you can find and fix problems along the way.

You don’t need to wait till the end of the month to disclose your recommendations which may become untimely by then. For example as soon as you find out that a TV campaign is going to get live tomorrow, you can start doing the necessary preparations to correlate online traffic patterns with TV ad times.

When the TV ad actually goes live, you can start your correlation analysis. You would then know that all the spikes in traffic that you get during that time are probably due to the TV ad. You can create an annotation in your Google Analytics report to remember that. So you are analyzing and finding solutions while the campaign is still running.

In a non-agile environment, you would wait for the TV campaign to get over. You would then study the impact of TV ads on your business bottomline may be days or weeks later.  This will result in unnecessary delays in delivering recommendations.

You may be able to deliver more accurate recommendations (because you have got a lot more data) but that would be untimely.

In agile analytics we don’t focus on perfecting data collection. We focus on making timely decisions and rapidly deploying solutions with ‘good enough’ data.

6. Stop being a perfectionist

Many people see perfectionism as a positive trait for them, something to be proud of. But perfectionism is the root cause of procrastination and the arch-enemy of agility.

For example, if I keep perfecting my blog post, to make it better and more informative then there is virtually no end to it. My quest for perfection will stop me from publishing this article because I will always manage to find something new to add or something new to edit.

So if you wish to be more agile, more timely than you need to suppress your urge for perfect data. You don’t need to be 100% sure before you make decisions. You make decisions with good enough data. Even if you fail, you fail fast and you fail cheap.

This is the beauty of Agile Analytics. It will help you get more done faster.

7. Do not conform to any particular process

Since you can’t decide beforehand where your analysis is going to take you, so you can’t tolerate any well-defined step by step process dictated to you.

In agile analytics you don’t have any well defined processes to find and fix problems.

Anyone who disagrees, doesn’t really understand agile methodologies in my humble opinion. The whole point of being agile is to move quickly and easily. Processes and procedures restrict agility. They act as a hindrance to quickly find and fix problems.

In agile analytics, once you have identified a problem, you can take any broad path to solve that problem. The aim here is to quickly deploy solutions and not to paralyze the outcomes by conforming to any particular process or over-analyze (also called the analysis paralysis)

Other article you will find useful: The Geek Guide to implementing Attribution Modelling

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