Case Study of Scalling South Asia’s ‘Most Promising EdTech’

Zubayer Wasit

Zubayer Wasit

I had a chance to work as Head of Growth at an uprising & pre seed stage ed-tech platform named Mentor Match at India. Mentor Match was awarded in The South Asia EdTech 100 is HolonIQ’s annual list of the “most promising EdTech startups” from South Asia.

Mentor Match helps in Instant 1on1 solutions through video call, app-based learning and live sessions. The core issue in short was they were unable to move past the 2X return on ad spend (ROAS) mark every month, despite increasing spend by 25%. The ask from the performance marketing (Google Search Ads, Display Ads, Facebook, Instagram Ads) team was to achieve a constant ROAS of 3X+ monthly at the current budget.

So, the goal was to achieve month-on-month (MoM) ROAS 3X for two quarters. The following are the steps taken to achieve a consistent 3X ROAS through Performance Marketing.

 

Phase 1 – Performance Benchmarking

Through this phase, we first identified the best-performing campaigns & assets from the past 6 months of data to establish trends & insights to help us optimize the current campaigns and launch new campaigns. The following factors were considered through this analysis phase –

  • Keywords & search term bifurcation based on performance metrics
  • Keyword bifurcation based on intent
  • Campaign-type performance bifurcation
  • Ad placements & distribution breakdown
  • Ad quality score & related performance metrics
  • Click-to-conversion rate and landing page quality analysis

 

We also cross-verified the Ads data against the data provided by the sales team from the CRM to attain insights on –

  • Best performing courses in terms of ROAS
  • High-ticket courses in term of ARPU
  • Location-wise performance, finding the highest CVR holding cities
  • Lead to customer conversion cycle & journey
  • Peak lead generation times & hours
  • First touch-point in terms of channel
  • Revenue distribution across acquisition channels

 

Phase 2 – Ad Campaign Strategy & Optimization

From the insights gathered from Phase 1, we derived a two-prong strategy to ensure a smooth transition at an account level to the new campaigns without any significant drop in campaign performance and overall brand revenues.

The idea was to keep the best-performing campaigns with minor optimizations and replace the non-performing campaigns with new ones, which we crafted based on the analysis & insights.

On the champion campaigns which we retained, we optimized mainly for –

  • Negative keywords
  • Keyword match types
  • Landing page content optimization

 

We stuck to the above handful of activities to ensure that the whole account doesn’t go into a learning phase, considering that we’re also parallelly launching new ad campaigns.

With the new campaigns also launched, we continued optimization sprints with the following factors –

  • Monitoring Google Search Partner vs Display Partner Network ad distribution
  • Experimenting with new offers & bundles
  • Regional language campaigns
  • Search trends & patterns

 

Phase 3 – Result Analysis

In addition to the regular Ad campaign performance analysis, we monitored the campaigns continuously and had weekly sprints for –

  • Lead-quality analysis: Lead to interested for Demo % & Lead to conversion %, top cities, top classes, week on week ad set & ad performance, gender analysis, day & hourly data & so on
  • Conversion ticket-size analysis: We had 2 different models, monthly package with unlimited doubts & 10 doubts small pack. Pricing also varied on models. We did cohort analysis for them
  • User journey optimization
  • Touch-point with the sales team for deeper understanding on the leads and enquiries
  • App Funnel Analysis: MoM CTR of headlines, descriptions, Traffic source breakdown, install-based by location, cohort-based con%, Class-wise split of CAC, MoM CAC, Class-wise split of Renewal rate based on session-based packages & subscription packages, renewal rate etc

 

#edtech #growth #ads #performancemarketing

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