Features
Banking
Our team developed a unique scaling strategy and created a limit price for cost per conversion metric to achieve this goal.
During the first week, AnData was able to train the artificial intelligence model by trying different rates and comparing the results. That way AnData’s algorithm created the optimal settings to achieve the set-up goal. This period is highlighted in green on the graph and you can visually see that the conversation was changing.
After the training week, AnData’s optimizer shows stable positive metrics — the number of lead conversions is growing while CPL is keeping the best possible price
Increase share of voice to beat competition with the competitor bank
The number of conversions increased 6x times. CPL also increased slightly but remained within the previously discussed target price. We maintain a stable CPL throughout the entire period of operation of the AnData optimizer.
Book a DemoInsurance
We launched an optimization model with a strategy of keeping the price point of CPL and increasing the number of conversions.
In the first few days, artificial intelligence is learning how to optimize the campaigns by comparing the number and cost of conversions at different rates.
Further use of AnData optimizer gave a steady increase in conversation volumes while retaining and reducing the cost of conversions — this is clearly visible on the chart of weekly indicators along the trend (dotted) lines
Increase the number of conversions within the same budget
The number of conversions increased by 20% while the cost of conversions reduced.
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