Customer journey analytics automation as a key to conversion and sales boost

The conversion rates growth, sales boost, and the bottom-line impact is the sacred Graal for eCommerce teams while an acquisition price growing year to year. Your online store can sell food, notebooks, or garden tools - if you have a significant volume of customer traffic, you think about conversion to checkout or sales growth on the same resources. The existing practice of conversion growth is based on the personal experience of e-commerce experts, marketing, product managers, or User Experience experts.

It is like the pre-web analytics epoch when experts made decisions about the efficiency of their sites with their gut.

You can say that every landing page has Google Analytics, Firebase, Mixpanel, or similar platforms in action. And my idea is simple - having dashboards and frequency graphs in a hand, you and your team still need to spend up to 50% of the time on user cases researching. How do you think why?

Why do eCommerce marketing and product teams spend dozens of hours per week on customer case analysis?

Because they want to know how users interact with an online store in reality to find points of revenue growth. In fact, a real Customer Journey is very different from the planned one. And that's why Google Analytics, Mixpanel, and other web analytics platforms are quite limited in capabilities to drive e-commerce towards sales boost. They show dashboards and frequency graphs with static metrics, but do not respond to the question of why and how.

The eCommerce product is not a usual landing page. This is a complex mix of components each of those impacts conversions and sales.

Here I want to divide customer interactions into three components traditionally served separately

They are -

1. Storefront or Interface interactions - visitors move through the interface to find information or a product to buy.

2. Backend interactions - the set of databases and microservices.

3. Service environment around the eCommerce - call center, delivery services, payments, digital advertising.

Components of a Customer Journey in eCommerce have to be aligned and tracked

To find insights you have to see the full picture, the map of customers' interactions. The map of traces or trajectories shows how precisely visitors move across the e-store with process metrics shown.

1. Storefront

And my point is you need to look at eCommerce like a complex product, but not a usual landing page. You have to build the user traces maps to discover the nature of visitors' and customers' interactions. If you don't a marketing team lost the knowledge about user behavior. Insightarc complements the functionality of web analytics and BI tools using events data to discover and build that kind of map automatically. Insightarc automatically collects user events and builds a dynamic map of interactions with the online store. The map enriched with process issues data and conversion rates metrics to solve the problem of conversion rate and sales growth in an effective manner.

All online businesses are unique in terms of customer segments, visitors' traffic sources, product feed, so some of the information provided may not be specific to one industry but can be tweaked to fit different needs. To fully comprehend how the e-commerce system performs needs to understand how leads function.

Insightarc automatically collects user events and builds a dynamic map of interactions with the online store. The map enriched with process issues data and conversion rates metrics to solve the problem of conversion rate and sales growth in an effective manner.

All online businesses are unique in terms of customer segments, visitors' traffic sources, product feed, so some of the information provided may not be specific to one industry but can be tweaked to fit different needs. To fully comprehend how the e-commerce system performs needs to understand how leads function.

2. Technological backend interactions. And not only technological.

This is a slightly advanced level for e-stores with engineers who develop and support the complex structure of backend operations. Do you know that low system responsiveness pushes customers to leave online stores due to low customer experience? Known that mobile users are highly sensitive to that indicator, especially iOS users.

But in practice backend operations (product databases, price databases, and many others) are managed via engineers monitoring tools. As result business owners do not know about the correlation between backend performance and conversions (revenue). In my opinion, eCommerce being managed as a tech-product, business owner or product manager has to see the full end-to-end process to avoid misleading regarding user experience.

I could say more. Often behind the customer request, let's say an order approval, is staying a real employee that manages orders, leftovers, warehouse operations manually. Actually why not, but it is critically important to track process performance, in that case, to make sure customers don't have to wait hours or days for confirmation. Otherwise, customers would buy in another e-store.

my message is the importance of measuring process metrics at such stages in conjunction with the main customer journey with the online storefront.

3. Service environment.

Usually, eCommerce experts miss the idea that the “last leg” impacts user experience quite more than a bad interface, just because customers personally meet a courier, speak with a call center or try to return the order. And those services are two-sided. The client's side delivers customer experience impact, and on the Operational side - bad performing and ineffective process boost operational costs.

Let's review the popular case of bad call center performance. If it takes 15 minutes to place an order for a customer, it may look good. But if the customer has to wait for a service support operator for 20 minutes to place changes on the order - it is quite bad.

In practice, most cases fall within the median, but what to do with other 15-20% customers? Are you ready to lose them as customers in the future? To resolve that task, the e-commerce team has to track the whole end-to-end customer journey with process performance indicators. As you understand, that task could not be solved in terms of traditional process analytics approaches just because it takes dozens of hours of analysis to build only one case. How would you manage several cases(cohorts, customer categories, product categories)? Usually, it requires 50 % of the time of the data analytics teams. Our team works to make that job automatic.

This is only one case given. In eCommerce teams routine there are similar cases as chatbots, delivery service, order returns operations cost scaling, etc. Insightarc automates this job by delivering transparent customer journey maps with process metrics in an automated manner.

The issue that most eCommerce SMBs place customer traces research as a ‘set it and forget i.

Once built, works for months and even years. Revisiting static journey maps you made a year or several months ago simply won’t suffice. E-commerce is a dynamic product where Customer Journey (user traces) changes depending on the source of traffic, kind of product research, or even new button implementation. To drive sales and conversion rates, you need an approach that is dynamic, measurable and actionable.

Another problem is the only top-tier eCommerce businesses have resources and capabilities to track customer behavior from those perspectives. That is why we decided to build the could platform for every eCommerce who wants to use the power of customers' behavior data analytics without huge expenses for data science, analytical agencies, and infrastructure costs.

Our principles are

• no infrastructure costs - Cloud-based

• no complicated host-to-host integrations - simple API to a data warehouse for product teams or manual upload for single experts.

• no installments required - web browser access for every stakeholder.

• data privacy - a client's data is a client's data. We are not an analytical agency, that's why we have no access to user data on any level of the technological process.

Automate Customer behavior analytics in e-commerce. Start to work with a process and customer journey efficiency in an easy manner. Receive actionable insights one-click > know their journey faults > make decisions > grow conversion rates and drive sales.