Optimizing Onboarding, Product Adoption & the Average Order Value For A SaaS HR Industry Leader 

Background and challenge

Our client, a SaaS HR industry leader (backgroundchecks.com), supports hiring managers through background checks technology, qualification screening, reference checks, and similar recruitment features and services. Despite operating for almost two decades, serving hundreds of thousands of customers and enjoying healthy revenue (over $70m ARR), the organization didn’t have an established, targeted and automated onboarding process in place.
The lack of a well-orchestrated onboarding process resulted in:
  • low conversions (registered user to paying user)
  • users were not adequately introduced to, and educated about the available product mix. As a result, the full potential for AOV and CLTV were not realized
  • high churn of small accounts (users)
  • low AOV of small accounts (due to smaller hiring demands)

Secondly, another aspect of increasing the AOV was to be handled through an up-sell campaign, which would complement the onboarding process.

Overall, our client was not maximizing their advertising ROI by letting a large portion of their users to churn. A substantial amount of unrealized revenue was left on the table.

Therefore, the main objective was to optimize onboarding (increase engagement, increase conversions, reduce churn, and increase AOV) and to implement a re-engagement and up-sell campaigns.

App Marketing Minds were chosen to create and execute this initiative based on our portfolio of other successful onboarding projects, just as our expertise in B2B SaaS.

Approach and project delivery

The project commenced by an extensive audit of their current onboarding processes, the product offering and customer segments (retention, drop-off points, customer journeys).

We also analysed which user segments will benefit the most from the numerous products and product combinations (our client had over 16 different products which could be mixed, depending on the use case, just as different ”packages” of these products bundled together).

Secondly, we considered how users’ past buying patterns influence sales of different products.

The audit provided important data and insights to form the stepping stone of the entire campaign – the strategy, just as for benchmarking and outcome expectations.

Next, we created onboarding campaigns/streams based on segments with specific characteristics, which can overlap. This complexity was managed by careful orchestration of marketing messages in every given scenario.

The segments were created using static and also dynamic criteria, i.e.: different traffic sources, their drop-off in the onboarding process, organizational type and size, or revenue per account in different time segments (revenue brought in the first OR month, three months, six months, twelve months etc.).

Once the overall strategy and steps were established, AMM set-up Intercom.com to deliver all the emails and in-app messages to different segments, especially utilizing the in-app behaviour features, sending smart email campaigns based on the actions of users (email, product usage, purchases).



Increase in sales assisted requests
Increase in overall engagement
Above industry average email KPIs
significant increase

The project resulted in than 18 distinct campaigns with over 40 emails or in-app messages addressing a varied range of users, with the aim to educate, sell, nudge/remind or engage (action-based). The marketing communication varied in channels and content – from longer use cases, case studies, messages feeding back to sales teams, proactive enquire if assistance is needed, or simple industry updates, to punchy and fact-based sales emails.

Outcome 1:

Considering this project holistically, AMM achieved the first objective of developing an integrated onboarding plan that provided accurate reporting of users and their sales journeys, and which effectively captured disengaged users that could be to proactively engaged in order to prevent churn.

Outcome 2:

Our onboarding campaign that used product education, product marketing and sales support with behaviour-based targeting resulted in increased engagement and product adoption.

In terms of KPIs, more than 75% of all communication achieved above industry average engagement metrics. Secondly, we noticed more than 45% increase in sales teams assistance requests. The messaging of these was formed around creating a ”package” of products to suit their specific needs – this directly increased the AOV.

Outcome 3:

We set-up 2 re-engagement campaigns which significantly improved conversions. Both during the onboarding process, and following the first purchase. The latter re-engagement campaign directly contributed to the AOV of smaller accounts with irregular hiring/less demanding staffing requirements. This particular segment was targeted by messaging that reminded users that ongoing background checks are as important as the initial screening, as only a miniscule percentage of offenders informs their employer.

Outcome 4:

A further effort was made to ”SaaS-ify” our client’s pricing model to increase the AOV. Despite operating a cloud-based solution, apart from the larger accounts with multiple locations, many customers were purchasing their HR solutions irregularly. Because of this, we introduced another product which would regularly conduct a specific types of background checks and screenings. 

Outcome 5:

In addition to recommending packages and directing users to individual sales calls, periodic screening, and re-engagement campaigns, we also supported the up-sell efforts through creating a new stream of smart automation to offer complementary products if one of the aforementioned alternatives were not purchased.

Outcome 6:

Lastly, we introduced a streamlined and effective NPS collection system that fed the sales team and our onboarding strategy with valuable data for improvements. This allowed us to identify areas of improvements in terms of product strategy and onboarding strategy directly from the users based on their own experiences, rather than analysing an aggregate of user behaviour data points. 

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