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Pay-per-click digital campaign optimization
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Pay-per-click digital campaign optimization

Can Machine Learning optimize pay-per-click budgets and keyword choices?

Pay-per-click and search represented 45.1% of the total internet advertising revenue of over $100B in 2018. Needless to say that the budget allocated to digital marketers is significant. It is also scrutinized and under constant pressure to deliver results, i.e., revenues and optimization. Considerable expertise is required to build a competitive and cost-effective pay-per-click advertising campaign. 

Assisting an advertising specialist in choosing the right keywords to optimize the pay-per-click budget of the company has unrivaled business value. Digital marketers often have data from previous digital marketing campaigns with the associated conversion rates (whether it be in terms of traffic or actual sales). The most popular searches are volume-based and attract a plethora of unqualified leads. They tend to drive more traffic to the campaign but not necessarily proportional conversions. The goal is to pick the most effective keywords to attract exclusively prospective customers.

Problems to solve

  • Is a keyword worth its cost in pay-per-click?
  • Which keywords, for which pay-per-click cost, will attract actual customers?
  • Can the potential for conversion of a keyword be estimated?
  • Benefits of TADA for pay-per-click
    Campaign Optimization

    70% of marketing managers view Return On Investment (ROI) as a top performance metric. 

    The two main challenges of the digital marketer are maximizing the profits for the advertiser based on a specific budget and improving the traffic on their website. Hence selecting the most effective keywords and their bids become a key asset to their marketing portfolio. Google Ads provides two essential variables: Global Monthly Searches and Competition, to evaluate a keyword bid.

    Digital marketers often have data available from previous digital marketing campaigns with the associated conversion rates (may it be in terms of traffic or of actual sales). The data they own is Small Data because they are in the order of tens, hundreds, or thousands. Using TADA, the Machine Learning platform from MyDataModels targeted at Small Data, marketers can manage the process of Budget Optimization for the multiple keywords of a campaign. TADA is accessible to anyone. No training is required—no need to be a Data Scientist or to code either. Results from previous campaigns can be uploaded together with the Global Monthly Searches and Competition for each keyword and the click-through rate, impressions, average cost-per-click, and conversion rate to find the combination of keywords with bids that maximize the campaign profit. 

    MyDataModels provides a self-service Machine Learning solution to marketing professionals searching to optimize they pay-per-click campaigns and looking for the most efficient Google Ads keywords.

    TADA brings new possibilities to digital marketers

    Combining bidding strategies and traffic and sales prediction through the use of TADA, the Small Data Machine Learning platform from MyDataModels can change the game for digital marketers.

    “The use of Machine Learning is a key differentiator in optimizing a pay-per-click campaign.”

    The organizations demand more and more proof of the contribution of marketing to the overall organization’s revenues. By optimizing their marketing budget for paid ads through the use of TADA, they generate traffic and sales conversions, which take them ahead of their competitors.