Understanding the Anatomy of Conversion Optimization

Last Updated: December 8, 2021

Has conversion optimisation made you a millionaire?……No

In order to understand, what conversion optimization really is, what it can or cannot do for your business, and most importantly, where conversion optimization fits in the ‘analytics world’, you first need to look at the big picture i.e. business analytics.

Introduction to Business Analytics

Business Analytics (BA) is a practice of repeated and systematic exploration of business data.

However, there is no standard definition for BA. Depending upon who you ask (vendor or consultant), different people may come up with different or even better definitions of BA.

I have outlined a very basic definition of BA. 

Business analytics is carried out to optimize the overall business performance from: operational, strategic to predictive.

Business analytics (BA) and Business intelligence (BI) are not really the one and the same thing, though they are often used interchangeably.

For me, BA is more of an umbrella term, which includes: data engineering, data warehousing, data mining, business intelligence, predictive analytics, etc.

Following are the various stages of business analytics:

  1. Exploratory analytics
  2. Data engineering
  3. Descriptive analytics
  4. Diagnostic analytics
  5. Predictive analytics
  6. Prescriptive analytics
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#1 Exploratory analytics

Exploratory analytics is carried out to determine what data need to be collected, measured, and monitored.

Data scientists/analysts work on the data collection requirements.

#2 Data engineering

Data engineering is carried out to build and maintain data management systems/architectures for:

  • Collecting and monitoring data
  • Bringing all of the business data together (data warehousing)
  • Visualizing data to understand large data sets (data visualization)
  • maintaining data quality.

Data engineers are responsible for the overall availability, usability, and security of data (data governance).

Data engineers do all the ‘building’ (software engineering) work. So if you need an algorithm, predictive model, prototype for data mining and modelling, they can build it for you. 

However, it is usually not the job of data engineers to determine data collection requirements or do data analysis. That’s the job of data scientists/analysts.

Data engineers work closely with data scientists/BA people to understand their requirements and build systems/architectures which meet their requirements.

#3 Descriptive analytics (business intelligence)

Descriptive analytics is carried out to find hindsight i.e.

  1. What happened
  2. When it happened
  3. How much/many times it happened
  4. Where it happened

Under descriptive analytics, a large set of data can be visualized (through data visualization tools) to understand it better and to identify patterns in data sets (data mining)

#4 Diagnostic analytics

Diagnostic analytics is carried out to find insight i.e. why it happened or is happening.

Here data science and its subsets: maths, statistics, and econometrics really come into the picture.

#5 Predictive analytics

Predictive analytics is carried out to provide foresight i.e. what will/could happen.

Correlation and regression analysis is carried out to predict future trends/outcome.

#6 Prescriptive analytics

Prescriptive analytics is carried out to benefit from foresight.

In prescriptive analytics, BA (business analysts) give recommendations on how to benefit from predictions/future trends and/or how to mitigate future business and marketing risks.

All of these BA stages are just the tip of huge icebergs. It take many many years, to master each BA stage.

For example, predictive analytics in itself is a big industry and there are conferences (like Predictive Analytics World) organized just around this topic.

Now you may ask:

Where does ‘conversion optimization’ fit in business analytics”?

Almost every business has got a website these days, whether or not they sell their products/services online. So they need professionals who can use data to optimize the online performance of their business, to meet their business goals (brand awareness, traffic, conversions, sales, etc).

Here there is a strong emphasis on optimizing the “online performance”. 

Data can also be used to optimize the “offline performance” of a business and to take a wide range of business decisions from operational, strategic to predictive.

For that, we use business analytics technologies like supply-chain analytics, predictive analytics, data mining, etc all of which are not web analytics or conversion optimization.

Web/digital analysts are hired to optimize the online performance of a business.

They are responsible for analyzing and optimizing any online and offline footprints of a business, as long as they can be measured, classified, or categorized and are used to optimize the [online performance] of a business.

Again there is a strong emphasis on optimizing the “online performance” here.

Data can also be used to optimize the “offline performance” of a business but that is not necessarily web analytics.

Conversion optimization is a subset of web analytics. Web page designs are evaluated and optimized for conversions through various tests (A/B tests, multivariate tests, usability tests, etc) and voice of customer analysis (surveys, feedback, market research report, etc).

Conversion optimizer is not a business analyst

A data scientist is usually the senior-most data analyst who has got extensive knowledge and experience in the application of data science in a particular industry. They usually hold a PhD in maths, statistics, or computer science.

These are the people who work in the capacity of business analysts.

A data scientist may head a whole team of data analysts. Among these data analysts, there could be analysts, who are specialized in analyzing web/digital data. Such analysts are called ‘web/digital analysts’.

Depending upon the size of a company and organization structure, there can be one or more web analysts.

If there is a team of web analysts, then there are going to be junior and senior web analysts within the team and there is also going to be, one senior most web analyst, who heads the team.

The senior-most web analyst often report to data scientist (if there is one) or to C-level executives (CMO, CFO, CEO, the board of directors, etc) directly.

Conversion optimizers often work alongside web analysts and report to the head of the web analytics team.

Again depending upon the size of a company and organization structure, conversion optimizers may include a whole team of UI and UX experts, or just one ‘guy’ doing all of the conversion optimization and web analytics.

In order for a business to truly grow, you need to optimize every aspect of your conversion funnel from operational to strategic.

A business analyst is in a position to optimize every aspect of your conversion funnel. He knows about and deals with: supply chain analytics, predictive analytics, data mining, business process modelling, stakeholder management, etc.

He/she often work in-house, has more control over day to day business operations and marketing activities, and directly deal with key stakeholders.

So unlike conversion optimizers, the business analyst can actually optimize the whole business process from: operational, strategic to predictive.

Conversion optimizer has control over the performance of following metrics (provided he/she control every aspect of the online presence and the corresponding offline footprints of a business):

#1 Gross Profit from Online Sales – the profit after online marketing costs.

Gross profit = online sales – marketing cost (oversimplified definition)

#2 Return on Ad Spend / Return on investment – This metric is used to evaluate the efficiency of your investment. You spend X, you got 2X, 3X… etc in return.

#3 Cost per online lead – the average cost of generating an online lead.

#4 Cost per online acquisition – the average cost of acquiring a customer online or generating online sales or other conversions.

#5 Sales per online acquisition – the average revenue generated through an online acquisition.

#6 Per session value – the average value of a session to your website.

Per session value = total sales / total sessions.

#7 Online Conversion Rate – the percentage of sessions that resulted in goal conversions or ecommerce transactions.

#8 Average order value – the average value of an ecommerce transaction.

#9 Task completion rate – the percentage of people who visited your website and completed your desired task.

However, the conversion optimizer has little to no control over:

#1 Operating Profit Margin – This metric is used to determine the effectiveness of your business in keeping operating costs in control. The Operating cost is the ongoing cost of running a business, product, or system which is beyond the control of a conversion optimizer.

#2 Net Profit Margin – This metric determines the effectiveness of your business in converting online sales into net profit.

#3 Net Promoter Score – This metric measures the likelihood of your customers to recommend your business to a friend, relative or colleague. The two factors which play a huge role in getting referrals are:

#1 After-sales service

#2 Satisfaction with the use of purchased product/service.

Both of these factors are beyond the control of a conversion optimizer.

#4 Online Client retention rate – This metric measures how good your business is in retaining online customers. The performance of customer support (which plays a huge role in retaining clients) is beyond the control of a conversion optimizer.

#5 Phone Call Conversion rate – This metric measures the percentage of phone call leads which resulted in sales. The performance of call center staff (which is actually responsible for converting phone calls leads into sales) is beyond the control of a conversion optimizer.

Conversion optimizer has very little to no control over product pricing, product packaging,  product positioning, merchandising, order fulfillment, management effectiveness, day to day business operations, after-sales service, etc all of which play an important role in improving the overall performance of a business.

So it is important to understand what a conversion optimizer can do and can’t do for your business.

The majority of businesses, can not afford the luxury of hiring a data scientist (they are very expensive to hire, very short in supply and in great demand).

The majority of businesses also often, do not understand the difference between a business analyst and digital analyst/conversion optimizers.

This is also because of the misleading advertisement, they often come across, done by some rogue conversion optimizers, who promise to make them “a ton of money” in the name of ‘conversion optimization’.

As you know it by now, business analysts have got entirely different skill sets than conversion optimizers and/or digital analysts and they need to be hired and trained in-house.

Consequently, a conversion optimizer/digital analytics can not work in the capacity of a business analyst.

So next time you ask yourself this question, why conversion optimization has not made you a millionaire so far, keep the following points in mind:

You can’t just A/B test your way to the top

In order to truly grow your business, you need to optimize every aspect of your conversion funnel from operational to strategic.

Just optimizing the web experience for conversions is not enough.

Don’t get mislead by false advertisement

“150% improvement in conversion rate”

Sound familiar?

A 150% increase in conversion rate means nothing if there is little to no improvement in sales.

You need to ask yourself the following questions:

  • How this conversion rate metric was calculated?
  • Is it in aggregate form or segmented?
  • Did they increase spend to improve conversion rate?
  • When was this conversion rate calculated? Was that the peak season?
  • Does this conversion rate improvement really mean anything?

With so many questions and confounding variables, it is hard to measure the performance of a conversion optimizer who boasts of increasing the conversion rate but shy away from disclosing the real result i.e. increase in online sales and gross profit.

Don’t get mislead by CRO case studies

“How we increased ___________ by ______”

Sound familiar?

“Technique X worked for Company Y in particular instance Z, so it is obviously a technique which will work equally well for your business” –  this is what case studies communicate to average Joe when they are used as marketing material.

Every business and industry is different, what works for one, may not necessarily work for another.

Besides, even small changes lead to big wins, if your business has strong sales potential.

On the other hand, if you are a start-up or a small business, then it won’t be easy to optimize your website for sales.

So next time you read a case study, take the size and sales potential of the business mentioned in the case study into account.

One doesn’t need a lot of skills to make an extra £50k for a company whose turnover is already over £1 million. However, it takes a lot of skills and effort to grow a small business.

Understand statistics

” You can run A/B tests 24 hours a day, 7 days a week, 365 days a year and still won’t see any improvement in sales if you don’t understand the statistics behind such tests.”

Data sampling issues, underpowered hypothesis, statistical significance issues, underpowered tests, overpowered tests, poor data sample, confounding variables, etc can easily skew your test results and give you imaginary lifts that will never translate into actual sales.

Related Articles:

Bare Minimum Statistics for Web Analytics

Understanding A/B testing statistics to get REAL Lift in Conversions

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Understand econometrics

Econometrics is the application of mathematics, statistical methods, and computer science, to economic data and is described as the branch of economics that aims to give empirical content to economic relations.

– Source: Wikipedia https://en.wikipedia.org/wiki/Econometrics

” According to the law of diminishing returns, if you keep adding more of one unit of production to a productive process while keeping all other units constant, you will at some point produce lower per-unit returns.”

– source: How to allocate Budgets in Multi-Channel Marketing

So for example, if you keep pumping more money into an Adwords campaign without changing the present form of the campaign, at some point you will reach the point of diminishing returns and once you cross this point, your conversion rate will go down and cost per acquisition will go up.

Because of this reason, you can’t double your sales just by doubling your marketing budget.

It doesn’t work that way.

Similarly, in the grand scheme of things, the whole conversion optimization process is just one unit of production.

And if you keep adding more of one unit of production to a productive process while keeping all other units constant, you will at some point produce lower per-unit returns.

What that means, if all you are doing, to improve your business bottomline, is to solely rely on your service provider to optimize your website for conversions, then according to the law of diminishing returns you won’t go very far in your business.

You need to do a lot more than just optimizing your website for conversion.

You need to work on improving management effectiveness and fix operational and strategic inefficiencies.

Conversion rate is destined to decline

Ideally, your website traffic should increase over time. But it won’t always increase in proportion to conversion volume. Because of this reason, your conversion rate (which is a ratio of conversion volume to traffic) is destined to decline over time.

The ever increasing traffic on your website will always tend to lower the conversion rate. You will always get some traffic which won’t convert, no matter what you do. Not every session or user can lead to conversion.

Despite all these shortcomings of conversion rate metric, every analytics tool on the planet puts each and every visitor/session into the conversion funnel while computing the conversion rate metric.

So this whole idea of optimizing for conversion rate is innately flawed.

You need to optimize for conversion volume (like sales, leads, etc).

Related Articles:

Here is Why Conversion Volume Optimization is better than CRO

What Matters More: Conversion Volume or Conversion Rate – Case Study

If you are not agile then conversion optimization is not for you

If you take a month, to add one piece of code to your website then conversion optimization won’t benefit you much.

In order to respond fast to the ever-changing needs of your customers, search engine landscape, social media landscape, and competitive landscape you need to adopt agile analytics methodologies.

What that means is, you need to learn to deploy solutions weekly if not daily.

In Agile Analytics, the success doesn’t come from the level of insight you get or volume of tracking implementations you deploy but it comes from your ability to adapt rapidly and cost efficiently in response to changes in the marketing environment.

It comes from your ability to rapidly deploy solutions which solve your customers’ problems either wholly or in parts.

Related Article: How to use Agile Analytics to quickly solve your Conversion problems

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About the Author

Himanshu Sharma

  • Founder, OptimizeSmart.com
  • Over 15 years of experience in digital analytics and marketing
  • Author of four best-selling books on digital analytics and conversion optimization
  • Nominated for Digital Analytics Association Awards for Excellence
  • Runs one of the most popular blogs in the world on digital analytics
  • Consultant to countless small and big businesses over the decade

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