Using Digital Analytics for Quick Math on Click and Convert
Investments in digital products can create thousands of unpaid ambassadors for the business. Read on to know how.
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Optimising digital marketing efforts and achieving higher ROI with digital analytics
Digital analytics is a broad category. Traditionally, businesses have used digital analytics to track digital marketing campaigns and see how those campaigns led to website conversion. Conversion could be different for each type of business. For an ecommerce business, it might be a purchase. For a hospitality business, it might be a flight or hotel booking. By tracking how often customers click on digital advertising and convert, businesses can view the advertising costs associated with the same digital advertising to compute return on advertising spend (ROAS). By combining this data, businesses can determine which digital advertising merits increased investment and which should be deprioritised.
In reality, however, it is more complex than this. Customers often leverage multiple paid and unpaid channels and campaigns before converting. As a result, businesses may need to use multi-touch attribution or other attribution techniques to assign credit to the various channels and campaigns that contribute to conversion if they want to optimise their advertising ROI.
Businesses should be able to track conversion success, attribute it to different channels and campaigns and look at long-term conversion success. Legacy digital marketing analytics products often stop at the initial conversion event but fail to account for customer behaviour over the subsequent months and years. For example, Google Analytics limits attribution to a 90-day conversion period. But some campaigns take longer to convert, making that conversion period insufficient.
Companies today need a solution that combines marketing analytics with product analytics so that businesses can analyse digital advertising channels and campaigns with downstream digital product behaviour. An integrated solution provides a more nuanced view of digital marketing efforts and increased advertising ROI. For example, imagine a digital marketing campaign that attracts 50 new customers who purchase an average of $27.31 worth of products, but most customers don’t purchase again in the next twelve months. Contrast that with a different campaign that brings in 25 new customers who make incremental purchases averaging $81.93 over the next year. The former campaign may look better, but the latter generates more revenue and loyal customers. Over time, the latter campaign would likely generate more ROI, but if the digital analytics platform cannot combine marketing and product behaviour over the long term, the business may use data to make incorrect business decisions.
Another trend is the adoption of product-led growth (PLG) motions. Product-led growth is a different approach to customer acquisition in which businesses rely more on their digital products to generate customers than traditional marketing campaigns. Targeting customers through paid and organic advertising has become complex and expensive as customers leverage more channels. Many businesses struggle to generate ROI on advertising campaigns. Instead, many leading brands have decided to spend more on their digital products in hopes that customers will hear about them through word of mouth. Investments in digital products can create thousands of unpaid ambassadors for the business. Digital advertising doesn’t have compounding effects, so if businesses stop spending on advertising, they will stop getting new customers. But many businesses have found that investments in digital products can have a synergistic effect in the marketplace.
Product-led growth, however, requires businesses to track all aspects of product usage to improve the product experience. Without this, product virality won’t occur. Therefore, digital analytics is a core component of a product-led growth strategy. The faster businesses can turn product usage data into insights and product improvements, the more likely they will gain new customers through product-led growth. Product-led growth can have the best ROI of any marketing strategy if successful.
Understanding customers through digital marketing channels and tactics
The best way to leverage digital analytics to understand customers and identify the most effective digital marketing channels and tactics is through segmentation. Segmentation, or cohorting users, places users into discrete groups based on their behaviours. For example, a business may create a cohort of customers who have added products to the shopping cart but have not purchased them. These “cart abandoners” can be added to a cohort and re-marketed via additional marketing campaigns. These cohorts can be generic or very specific. For example, a retailer may have hundreds of customers who have abandoned pants in the shopping cart. This user cohort can be sent a personalised email that reminds them about the pants they left in the cart, possibly with a discount offer, and maybe push some popular cross-sell products. This is an intelligent use of digital analytics to optimise digital marketing and improve overall conversion.
Cohorting can be taken further by extrapolating data from known to unknown customers. For example, let’s imagine that digital analytics data suggests that customers who purchased the most jeans were women between 18 and 25 and from London and Paris. With this data, marketers can leverage advertising networks to target the specific product (jeans) to a particular audience (women, 18-25, London+Paris). This level of targeting can dramatically increase the marketing effectiveness of new customers by leveraging known behaviour from existing customers in the digital analytics platform.
Moving beyond short-term conversions of digital marketing metrics
Most businesses have used the same metrics to measure digital marketing campaigns. These metrics include the following:
- Entries/Visits – How often a campaign brought customers to the website/app
- Bounce Rate – How often customers came from a campaign but took no action
- Conversion Rate – The rate at which customers complete a conversion divided by the entries/visits
- Return on Ad Spend (ROAS) – Revenue generated divided by the cost to acquire customers
Beyond these metrics, we also recommend that customers focus on the lifetime value of marketing efforts. Campaign lifetime value is similar to return on ad spend but with a much longer time horizon.
Businesses should also create North Star metrics beyond just conversion. There are actions that customers can take within digital products that aren’t monetary conversions but do indicate that customers are receiving value. These North Star metrics may be more important than short-term conversions. For example, imagine that a business like Spotify had a North Star metric of “playlist shares.” Spotify may have found that sharing playlists indicates customer loyalty and long-term conversion. Even though Spotify may not earn revenue when users share playlists, they may consider an advertising campaign that led to many playlist shares a success, understanding that revenue will come later.
Transforming digital marketing campaigns for business success through A/B testing
A/B testing can be a powerful way to try new content or functionality. When digital marketers use marketing campaigns to drive customers to the website or mobile application, they want to optimise the experience. A/B testing provides a way to personalise the marketing experience so every customer doesn’t see the same thing.
Imagine you manage a B2B business that sells CRM software and are running a campaign to inform people about your product’s recently added customer service features. You may purchase paid search keywords related to “customer support” and send those that click to a marketing landing page to explain your value proposition. But instead of having just one landing page, you can use A/B testing and experimentation products to try many different versions of the landing page. Additionally, you can leverage A/B testing products to personalise the content using other known data. Suppose the person clicking on the advertisement has been to your digital property in the past. In that case, you can leverage past digital behaviour to show content or features they have previously expressed interest in. Or you can leverage account-based marketing technologies to view their industry and offer relevant case studies specific to their industry. Both of these techniques allow you to leverage A/B testing and experimentation to view how different versions convert compared to others, which leads to improved conversion and maximises your advertising budget.
Challenges businesses face when it comes to digital marketing optimisation
Without a doubt, the biggest challenge facing businesses around digital marketing is privacy. Many of the best aspects of digital marketing rely on the ability to collect accurate data and stitch together customer behaviour across multiple devices and sessions. Knowing if a customer visiting your digital property is the same one who was there last week and last month is essential. But as customers switch devices and use browsers that automatically delete cookies, knowing if the customers viewing your digital properties have been there before is becoming increasingly difficult. Consumers have spoken and told the marketplace that they prefer to be anonymous. It is likely that each year, a high percentage of customers will be anonymous when they visit digital properties.
If businesses cannot determine if customers on their digital properties are repeat customers, it decreases their ability to attribute success to digital marketing efforts. The anonymisation of users can make it look like the last marketing campaign is the only one that led to success. This can skew marketing decisions toward channels and campaigns that happen last, even though other campaigns and channels are essential. The inability of businesses to accurately identify users can have dramatic ramifications on optimising digital marketing campaigns and budgets.
Businesses can mitigate this challenge by encouraging customers to create authenticated accounts with their digital property. Once customers create an account, they consent to be tracked, and all data can be associated with their known customer profile. This also creates a direct relationship between the customer and the brand instead of relying on advertising networks to find and identify customers.
Another way for businesses to mitigate the privacy challenge is to invest in “incrementality” technologies. Incrementality is a new approach that leverages machine learning and experimentation to optimise advertising spend. By constantly adding and removing digital advertising, incrementality can compute which advertising combinations lead to incremental conversion without knowing whom the customers are clicking on the advertisements. Incrementality represents a way to optimise marketing without infringing on customer privacy.
Best practices to maximise digital analytics by unlocking business potential
Achieving success in digital analytics is not as straightforward as it may appear. While it may seem that all you need to do is place some code on your digital properties, the reality is that businesses need to carefully consider the questions they want to answer and map those questions to the data required to answer them. A digital analytics implementation that aligns strategic business questions with data and reports is crucial for success. Moreover, it is essential to ensure that the digital analytics implementation is set up correctly and that the data collected is of high quality. Those accessing digital analytics data must also be trained to understand what it represents and how to use it effectively. Dashboards and reports must be created and verified, including measuring metric formulas. Even after all of this, humans must interpret the data correctly and draw reasonable conclusions about what the data indicates. Finally, the analysed data must be synthesised into insights and actions to improve digital products, conversion rates, or advertising spending.
It is evident that much work needs to be done before businesses can reap the benefits of digital analytics. Based on best practices, we recommend the following:
- Spend time identifying the specific business questions you want to answer upfront
- Collaborate with team members on data to develop the best insights
- Take a long-term view of data to view lifetime success versus short-term success
- Provide data self-service so that anyone within the business can obtain the data they require instead of hoarding data in a small group
- Document the changes to digital products and marketing efforts due to digital analytics data
- Quantify the incremental revenue or cost savings that digital analytics helped produce
