Best Guide for Building a CRO Strategy for the Future
With increased demand for agility in software development, marketers are required to reimagine their conversion rate optimization (CRO) strategies. What worked well in the past does not ensure the desired outcomes in the future.
This is due to market dynamics plus behavioral science. Modern consumers are smart and know how and when to adapt to technology. They are surrounded by hordes of data and thus are good at filtering it out.
To grab the attention of these consumers, you get miniscule amount of time and that’s how fast your CRO strategy should be to guide your readers deep into your sales funnel.
In future, the demand for a quick grabbing CRO strategy is going to be more essential than ever.
But how do you make your CRO strategy so fast?
According to WordStream, companies whose conversion rates improve, do 50% more tests on an average.
This means to refine your current CRO strategy, you are constantly required to perform experiments. In the fast-changing times, routine optimization of landing pages and multivariate testing are the only constants required to devise a successful CRO model.
To keep up with the modern times and prepare a future-ready CRO strategy, we advise you to experiment with the following factors. They resonate with today’s and upcoming CRO challenges and are imperative in the near future.
Essentials to Devise CRO Strategy in 2021
According to the thought leader BCG, consumers are well-aware of companies collecting their data and thus they expect personalized experiences in return.
Moreover, consumers are getting more inclined towards convenience.
To add on, if we consider the decreasing amount of time a brand gets to grab their attention, then personalization is the most reliable way to deliver your message.
It is what consumers demand, brings convenience and is a fast way to relate your brand with customer pain points.
One can personalize information at various levels and omni-channel implementation is the one that brings the best results.
However, to implement large scale personalization, companies face three main problems:
Due to excessive data collection, it gets difficult for bands to prioritize a piece of information over the other.
Lack of Tools
Omni-channel personalization requires rigorous monitoring. Companies often don’t have so many tools or knowledge of tools to monitor and analyse omni-channel performance.
After data collection, devising and implementing strategies for each user segment becomes difficult.
These strategies are often not applicable together.
How to Overcome these Problems?
Intelligent consumer segmentation is one way to get behind all aforementioned problems.
Creating buyer personas is a widely-used consumer segmentation practice.
However, when compared to modern times, it is too generic and stands pale.
This is because to overcome the personalization challenges that we just discussed, we need in-depth and precise information.
Apart from basic demographics, general buyer personas do not offer much.
Perhaps, personas created on the basis of behavioural segmentation are more useful.
They help you group consumer interests, devise a workable strategy and can be monitored using simple tools.
Instead of focusing on general demographic attributes, behavioural segmentation studies the following criteria on broad level:
This is a segmentation based on the benefits sought by consumers.
For example, while a consumer segment of a hair oil brand might be buying the product for its anti-dandruff properties, others might be purchasing for conditioning.
After recognizing the benefits that the majority of customers seek from your product, you can personalize your landing pages and email campaigns.
Some consumers are price-conscious.
They only purchase after getting the best price in the market.
Some focus on loss-aversion, i.e., they won’t buy without product guarantee or a return policy.
Similarly, some require some social-proofing and others like to conduct thorough research before purchasing.
According to Bain, companies can divide products into loyalist and repertoire categories, which generally indicates how likely consumers are to change brands for every purchase.
For companies with a high number of loyalists, the promotions and customer experience will change.
They are required to focus more on targeting existing customers instead of finding new ones.
If you sell products that consumers use on a frequent basis, such as grocery, you have a heavy-user customer base.
These are the people who tend to purchase your product on a routine basis.
They will be visiting your website from time to time.
On the other hand, a light-user customer base refers to those individuals who only buy a product once a time, such as computers and smartphones.
Based on these behavioural segmentation categories, you can optimize your different marketing campaigns, change the overall landing page and prepare effective CTAs.
For example, find loss aversion a common deciding factor in your customers? Highlight your return policy in the CTA.
If 50%-60% of your customers are loyalists, provide reward points on every purchase.
Personalizing your customer experience on multiple facets can bring a significant increase in conversion rate.
The advancements in internet technology, such as increased access to fiber optics, globally established 4G connectivity and now, the gradual emergence of 5G technology are introducing several changes in digital marketing.
Low website speed does not only mean that visitors will bounce back from your website quickly, but they will also receive a poor customer experience from your company.
To further elaborate, poor site speed can have the following effects on your website:
- Decrease your overall traffic, affecting any affiliate marketing campaigns initiated on your website
- Affect mobile browsing experience and drop search rankings (see mobile-first approach section below)
- Increase cart abandonment rate, especially when the customer was trying to load the checkout page
- Deteriorate user experience by slowing down interactive elements
- Create an impact on your overall brand image
Google Page Insight is a helpful tool that you can use to keep constant checks on site speed performance.
It calculates your website’s speed base on Core Web Vitals:
First Contentful Paint (FCP)
This includes the initial content that the browser loads on a webpage like text, images and other background graphics.
First Input Delay (FID)
Measures the response time of a web page when a user clicks on an interactive element.
Largest Contentful Paint (LCP)
Time taken to load the largest bits of information on a webpage.
Cumulative Layout Shift: (CLS)
The number of unnecessary changes in the webpage layout.
You can view all these vitals in the Field Data tab of Google Page Speed, which is more reliable than other tabs.
The Field Data tab measures site performance in relation to how it performs when someone visits your website in real time.
Improve Your Web Page Load Speed
You can take the following measures to improve your web page load speed:
Use a Clean Design
You do not want too many elements interfering with the user interface of your landing page.
Replace large elements with maximum storage and replace them with something more compact.
Also use compressed images for display.
You can compress high definition images up to 60% without losing quality.
Use of a Content Delivery Network (CDN)
Content management systems (CMS) like WordPress used a CDN to deliver content to web browsers.
CDNs work on a network of servers and thus are more powerful at delivering content than your regular web-hosting server.
Remove Third-Party Plugins
While extensions and third-party integrations (TPI) do increase the functionality of your website, they also add up to the overall size of a webpage.
If the TPIs you are using are really important, instead of removing them from your website you can disable them on specific landing pages.
Cache is the temporary data stored as special memory on the system.
It is a copy of data that is frequently exchanged between a browser and the server.
In layman terms, it is a small memory of your website stored on the server so that it does not have to begin the entire transfer process from scratch every time the browser generates a transfer request.
Google switched to mobile-first indexing back in September 2020.
This means that websites are now ranked based on how well they perform on mobile devices.
In mid 2021, Google will also release its page experience update that will consider the overall browsing experience an important ranking factor.
These two important updates alone call for a mobile-first development approach if you want to preserve and increase your conversions in future.
What Does Mobile-First Approach Actually Mean Though?
In the history of web development, websites have always been designed for desktop screens.
After their development, developers adjusted them for mobile phones.
The emergence of scalable, responsive and adaptive designs that readjust themselves to different screen sizes further simplified desktop to mobile transitioning.
In contrast, the mobile-first approach requires businesses to first develop their websites for mobile screens and then adjust those websites for desktop.
Thus, assuring higher performance and better experience on mobile screens.
Examples of Mobile-First Approach Designs
Below are some good examples of mobile-first approach designs:
Tesla Mobile-First Design
Apple Mobile-First Design
Evernote Mobile-First Design
Benefits of Using Mobile First Approach are:
- Clear, succinct and lag free designs
- Higher rankings in Google search engine results
- Easily manageable
- Fast load speed
- Appealing for eCommerce sites
Because of how the mobile-first approach requires brands to be highly selective on their webpage content, it is also known as the content-first approach.
Descriptive Data Analysis and Feature Engineering
eCommerce businesses collect tons of data today.
For every single transaction you have an item tag, category tag, price tag, vendor name, date of order, date of delivery, etc.
When such diverse information is compiled in one database, we call it Big Data.
Analysing this data in a meaningful way to find solutions to various problems is known as descriptive data analysis, which is not currently in practice at a large scale.
Efficient descriptive data analysis will help marketers resolve several unforeseen conversion problems in the future.
Descriptive Data Analysis vs Exploratory Analysis
Descriptive data analysis is different from exploratory analysis, which is performed in businesses at a wide level.
Exploratory analysis includes the rendering of valuable insights (such as you see on Hotjar and Google Analytics) to find patterns and prepare visual analysis.
However, exploratory analysis alone is not much useful in preparing an effective CRO strategy that provides data-backed answers to your low conversion rates.
For example, collection of information such as gender, age and cart abandonment rate is exploratory research and you can use this data to prepare buyer personas and launch various marketing campaigns.
However, going one step further and using this data to answer the question why you have a high cart abandonment rate is descriptive data analysis.
Maybe the cart abandonment rate is high when shopping from mobile devices only, this means you need to optimize your conversion rate on mobile devices.
Feature engineering refers to the process of extracting new information from the existing raw data.
It is usually performed by machine learning algorithms but can be also processed by explicit programming.
With the help of feature engineering, businesses can do reliable predictive analysis and optimize their websites for better conversion.
For example, the two features; A – the time taken by a visitor to add a product to its cart and B – The checkout time are raw data that can be easily collected via exploratory analysis.
However, deriving Feature C, i,e., the total time taken by the visitor to complete a transaction after adding a product to its cart is called feature engineering.
You can calculate Feature C by subtracting feature B from feature A.
Such type of feature engineering is very useful for conversion rate optimization.
For example, the business can reduce the checkout steps to reduce the time in Feature C or perform univariate and bivariate testing to find the reason behind high checkout duration.
Univariate and Bivariate Testing
Univariate and bivariate testing can assist in optimizing a landing page.
For example, you can test the positioning of sales information on your webpage or find the correlation between page length and conversions.
Giant MNCs and large businesses like Amazon and Uber perform descriptive data analysis and feature engineering on a regular basis.
However, startups and small businesses are often late to these practices.
In some scenarios, it isn’t possible for a marketing expert to perform complex data analysis and for this, you may need to hire a data science expert.
Competitive analysis is important to figure out what your competitors are doing to generate high revenue and predict their next move.
Besides CRO, competitive analysis also assists in improving your various digital marketing campaigns.
To profusely improve your CRO strategy with competitive analysis, you need to take care of the following:
- Find who your real competitors are
- Learn from their conversion practices
- Wisely implement the same on your website
Directly asking customers about market competition can tell you a lot about your competitors.
You can find similar brands providing similar services to your target region or audience.
In protected markets, you can find competitors from distant geographical regions having the same attributes as your target audience.
Just being aware of competitors and their marketing efforts can set milestones for your companies to accomplish.
What to Learn from Competitors?
First of all, you want to notice their unique selling points to understand the extra value that your competition provides but you do not.
There is no need to copy those selling points but you can come up with some better value to compete against these.
Promotions and Discounts
Maybe your competition provides a special discount on Sundays or just simply have a more lucrative array of discount coupons.
If this is the case behind the increased revenue of your competitor, then you may need to launch some discount offers in order to compete.
It goes the same for any promotions, such as partnerships with YouTube influences and affiliates.
Omni-channel experience is the demand of today.
It is possible that your competitor can be providing better support than your business and via diverse channels.
If that’s true, then more support channels is what your consumers demand and you should respond back to them.
Effective CTAs are more than attractive.
They tell readers a lot about your brand, your product and the benefit and also make them click.
Along with CTAs, headings and titles on your website affect the customer's decision making process.
Maybe you can learn a thing or two about copywriting by inspecting your competition’s website.
Upselling and Product Bundling
Maybe the sales of your competitor are high because it knows how to upsell and bundle products for generating more profit.
Here we are not referring to the product recommendations that appear at the end of the product page, but some well-thought combinations and upsell products that consumers are more likely to buy.
For example, ink with printers.
Although this is an easy example but with some thought and competitive analysis, you can find out which products of yours will go together the best.
You can review the overall customer experience of your competitor by placing a product order.
Keep a tab on how many delivery updates you get or any follow up emails.
Was their customer experience too smooth or sounded better than yours? Maybe that’s how they developed a loyalist customer group.
After completing the analysis, you need to gradually progress with the changes to ensure uniqueness on your website and prevent the loss of some genuine value that you were providing on your website from the beginning.
Conversion Attribution Model
Giving credit to the different touch points of your sales funnel can help you figure out the worth of your marketing efforts.
For example, a simple marketing campaign of a business includes various touchpoints and channels, such as social media platforms, business website, landing page CTAs, emails, etc.
Without knowing which channels contribute the most in your conversions, it is difficult to optimize the entire sales funnel.
Google Analytics supports various conversion attribution models to help you understand the value of your each touchpoint or marketing channel.
You can compare your funnel with these models to find out which channels you are overvaluing and which you need to pay more attention to:
This attribution model gives the entire credit to the very last touch point.
For campaigns with multiple marketing channels and interaction, the Last-Click attribution model is highly irrelevant.
However, you can still use it if you are solely dependent on organic results or PPC ads.
In this model, the first channel of interaction gets all the credit.
This is the channel that begins the buyer journey of the customer and can be valued as more important.
First-click attribution is more relevant than Last-Click but it still fails to help you understand the value of your other touchpoints.
Linear attribution equally divides the credit amongst all channels.
Though this model does account for every touch point and channel, it is still highly unreliable.
It cannot help you find undervalued and overvalued channels.
Time Decay Attribution
This attribution model increases the credit given to the different channels based on the order of interaction.
For example, the last channel gets the maximum credit while the first channel gets the least.
Time decay attribution model is quite reliable as it helps you organize your marketing channels but it can still fail to represent the real value of channels.
Last Non-Direct Attribution Model
The last touchpoint in the majority of sales funnels is a CTA or buy button.
Last non-direct attribution overlooks the last touchpoint and gives maximum credit to the one before the last click.
This model represents the reality of every sales funnel but neglects the contribution of the first few channels.
Last Ad-click Attribution
The last placed ad in the sales funnel gets all the credit.
You can use this model if you are highly dependent on advertisements but it cannot help you optimize other marketing channels.
Position-based attribution gives 40% credit to both first and last touchpoint of your marketing campaign.
It equally divides the remaining 20% to all the touchpoints that fall between the first and last.
This attribution model accurately represents digital marketing and is the most widely used model in 2021.
Google Analytics also allows businesses to create custom-attribution models based on their business requirements and marketing instincts.
It is easy to streamline your marketing actions by determining the most valuable ones.
By highlighting the most important touchpoints, conversion attribution models also help you reserve a company’s marketing resources, leading you towards the path of smart marketing.
As a digital marketer, do not expect quick results from conversion rate optimization.
Everything is based on experiments and it can take a while to know what suits your company the best.
However, a proper strategy can help you come up with better experiments based on actual facts and bring gradual progress with each minor optimization.
Also, do not feel reluctant to create a new strategy if a previous one fails.
The minimum time you should observe the results of a CRO strategy is 1-3 months before replacing it.
November 29, 2023
November 14, 2023
November 7, 2023