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How combining machine learning with human rights helps global digital money transfer business Monisnap to maximise profits

Goals
  • Develop global digital strategy to grow business and maximise conversions
  • Drive traffic through automation, freeing up personnel to focus on building business strategy
  • Combine automation with human knowledge to improve conversion rate
Investment
  • Smart Bidding: Target Cost Per Action (CPA)
  • Seasonality Adjustment
Results
  • 50% more conversions
  • 15% increase in conversion rate
  • 10% decrease in CPA

“It’s about family sending money to family. We wanted family and culture to be at the heart of Monisnap.”

- Jean-Baptiste Bouvier, Monisnap, CMO & Co-founder

About the company

2017

Monisnap launched

15

employees

100+

countries Monisnap operates in

About the goals

With an estimated 1.7 billion adults worldwide still unbanked, Monisnap’s founders wanted to help people working abroad support their relatives at home. Uninspired by the impersonal bank-like approach of existing transfer companies, Monisnap wanted to be connected to the people who used the service. ‘It’s about family sending money to family,’ explains Jean-Baptiste Bouvier, CMO and co-founder. ‘We wanted family and culture to be at the heart of Monisnap.’ Their 1-click digital solution lets customers transfer money via smartphone or laptop, eliminating the need for a recipient bank account.

Monisnap wanted to integrate this personal ethos into its advertising strategy too, establishing a network in over 110 countries that gathered data on local culture and events of national significance. Using this to tailor its marketing material, Monisnap hoped to attract more customers to its bankless transfer system by meeting the needs of their families with local dialect ads that spoke to communities back home more personally.

By combining these human insights and industry expertise with machine learning, Monisnap could understand the market fluctuations spurred by national holidays or favourable exchange rates. Google Ads’ automated bidding strategies enabled Monisnap to utilise seasonal interest more effectively and drive traffic by adjusting its advertising spend as part of an automated bidding strategy. This meant the company could achieve maximum exposure at minimal cost and would also allow Monisnap’s small team to focus on strategies for further expansion.

Investment

Monisnap had ground level data showing what devices recipients wanted to access transfers on and when they were most likely to be sent money. Inputting this into Google Ads Smart Bidding algorithm meant that they Monisnap could use it to inform bid values via machine learning, maximising returns by reaching the right potential customers at the optimal time. The automated nature of this process made it a very time-efficient option.

Monisnap chose a Target Cost Per Action (CPA) campaign to keep the CPA (i.e. per bid) low by bidding strategically through machine learning on ad space that would maximise traffic while keeping costs within a fixed parameter.

Monisnap also harnessed the power of Seasonality Adjustments – an advanced machine learning tool that informs the Smart Bidding algorithm of expected changes in conversion rates for future events, such as national holidays. This allowed them to meet surges in demand with a conversion rate adjustment – if the business expects conversion rates to increase during a festival, for instance, they can create a Seasonality Adjustment that looks for advertising space at a lower cost for a limited period.

How does Target CPA bidding work?

Target CPA bidding automatically finds an optimal bid for your ad each time it's eligible to appear, using historical information about your campaign.

Learn more about Target Cost Per Action

Results

50%

more conversions

15%

increase in conversion rate

10%

decrease in CPA

The secret to Monisnap’s success with Google Ads lies in the quality of the data collected from its network and the particular efficacy of automated campaigns in driving traffic. ‘It’s a match between Google machine intuition and our knowledge of human behaviour,’ says Jean-Baptiste. This profit-boosting formula has proven particularly successful for Monisnap, generating an impressive 50% more conversions at or below its target cost per action – that means 50% more custom, while the cost of each bid stays at or below a fixed maximum rate.

Monisnap’s 15% increase in conversion rate indicates that more people interacting with its ads were compelled to complete a transfer, a figure which speaks to the quality of its advertising material and the impact of the business’ ethos of personalisation. By presenting ads in local dialects that speak to local interests, Monisnap turns this personal connection with customers and their families into a benefit for the company. Since implementing Smart Bidding strategies, Monisnap has seen their average cost per action decrease. The company is now achieving conversions from their ad space at 10% lower cost – something Jean-Baptiste has described as ‘massive’ news for profit margins and for Monisnap’s future.

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