Every eCommerce brand in its growth stage reaches a phase where it experiences apparent stagnation. At this stage, there may also be a substantial amount of customer data available, and the brand may have made inroads into using this data to generate insights.
Using powerful insights and market cues to grow at this stage can help take the brand into a new phase of growth. Not acting on it can also cause competitors to take over, and result in less-than-ideal brand penetration.
In other words, it is time for the brand to focus not just on marketing analytics, but their entire shopping experience (product analytics) as well.
Usually, when we speak to eCommerce brand owners at this stage, we understand that there is a significant amount of confusion around what these two terms mean.
What precisely are these functions? Are we using them to their full potential?
Diving Deeper Into Product Analytics And Marketing Analytics
In eCommerce, the purpose of every analytics function is essentially the same- understanding and catering to the customer journey. The customer journey accounts for the entire ambit of experiences a customer undergoes, starting from awareness of your brand or business and lasting into advocacy, also known as the ‘delight’ phase.
In this context, marketing analytics centers on
Customer traffic sources
The path customers take to reach the conversion- which, in our case, means making a purchase
Ways to increase conversions while keeping advertising spending to a minimum. Product analytics, on the other hand, covers
User behavior at every touchpoint in the customer’s journey
How customers interact and engage with your eCommerce site
Potential issues in the user’s journey
It can be said that marketing analytics is a subset of product analytics which specifically deals with bringing people to the web store, while product analytics covers the entire ambit right up to purchase and delivery.
Product Analytics Capabilities
While Product Analytics offers several capabilities, some important and common ones include :
1. User segmentation: bucketing customers into groups with shared characteristics based on parameters like demographic data and behavioral data, as well as past interaction data.
For example, product bundles based on demographic data may reveal region-wise preferences for certain types of bundles. Product lines can be further explored and expanded in those areas for such user groups and performance metrics can track how the new products are faring across user segments and get more granular insights.
2. Cohort analysis: measuring customer engagement in cohorts of similar attributes over a span of time. This helps us understand how user groups interact with our brand in the long run.
For example, if certain cohorts show a high churn rate, it indicates issues with retention. Similarly, if certain cohorts are consistently returning to the site for repeat purchases, we can dive deep into the steps that are facilitating this and replicate it across other products or features.
3. Feature-wise engagement metrics: to figure out where specifically customers are facing problems and how long they spend on each step of the process. With such precise data, features can be modified to make the experience smoother and seamless.
For instance, a cohort with the elderly segment of the audience may find large icons easier to decode when navigating from one page to another.
As we can see, good product analytics come from sound data, which in turn is made possible by integrating the multiple channels of engagement and sales that a customer may be exposed to. For example, a customer might see the product on the Instagram Shop, navigate to the website, and leave without buying.
Another customer might land directly on the website, add to cart, abandon the cart and wait for a coupon code to arrive.
At scale, product analytics helps brands identify behavioral patterns as well as the user experience with the eCommerce brand.
Making Sense of Marketing Analytics
The core aspect of Marketing Analytics is understanding the drivers of growth. This growth is typically made through marketing campaigns that generate increased traffic, acquisition, and conversion.
1. The intent is to find the channels that result in the best Return On Investment (ROI) on a consistent basis. The insights usually tracked here are traffic numbers from various sources- social media, organic search, or paid campaigns, and the quality of visitors in terms of bounce rate and average session length. In addition, sales conversion and average customer value are also measured.
2. Specific to ad campaigns, we must also measure the Return On Ad Spend (ROAS). This key metric tells us which ad channels bring in the most high-intent, high-conversion traffic for us so we can allocate ad budgets accordingly.
As an example, let us consider the Google Analytics dashboard shown here. Among the marketing campaigns through various media, social media channels seem to be bringing in more traffic compared to email marketing or organic search. However, sales and conversion ratios may be better with email marketing.
As we can see here, every touchpoint is important and has a role to play in leading to the conversion. Marketing analytics allows us to assign the right importance to each marketing activity and channel, and helps us calculate the true cost of acquiring a paying customer.
As another example, high bounce rates, and low session duration can together indicate high traffic but a mismatch between what customers expected to see, and what they actually got. These insights become all the more important considering that growth cycles are often short and festive and sales seasons need to be leveraged to amp up the growth spurt.
Deriving Maximum Benefit From Product And Marketing Analytics
As we have seen above, product analytics and marketing analytics address different and complementary aspects of the customer journey and experience. Depending on your short-term and long-term business goals, you may need to prioritize one over the other, particularly in the early days.
An upcoming sales season would need more marketing analytics if the immediate goal is gaining new users. However, to turn these new users into repeat customers and thereby reduce our acquisition costs, we need to act on insights from product analytics.
As an eCommerce brand scales, both these toolsets are needed to maximize the customer experience and increase the average lifetime value of customers. If we deploy only Marketing Analytics, we can see an increase in traffic and even conversions but this may taper off if the user experience is neglected.
For example, product features that increase friction to action such as forced signups can frustrate customers and lead to cart abandonments.
All aspects of the customer journey are interlinked, which is why we need an integrated framework to capture both product data and marketing insights. Reading them in isolation can often cause business teams to come to the wrong conclusions, and thereby launch ineffective campaigns. For example, let us consider an instance where potential customers land on the website, browse through a few pages and then leave the site. Marketing analytics alone would indicate multiple scenarios:
The site visitors are new users and therefore do not know enough about the brand to make a purchase, or
The time spent on the site is fairly high, so running paid ads and bringing in more traffic may result in more conversions.
However, when we add product analytics to the mix, we begin to understand that the users probably got lost on the website and couldn’t find their way to the right product page. Or, they needed more information and didn’t know how to contact the store owner.
Even from such a simple use case, we can see that we need integrated analytics that helps us derive the right insights both from customer experience and marketing campaigns.
Tools like the Graas' Predictive AI Engine enable this integration, while also providing easy-to-implement insights for eCommerce brands. Growth combined with sustainability is the key to helping an eCommerce venture rise and realize its true potential.
Take a look at the Graas' Predictive AI Engine here to see what integrated analytics can do for you.