Case Study 1
                       

We decided to go out with a new procedure. From time to time, we will publish a case study about a particular client, detailing the problem, how we solved the problem and statistical data.

Case study #1 talks about a customer who sells to customers through his e-commerce site. The client invested hundreds and thousands of dollars a month in advertising on Google and Facebook, but did not receive adequate compensation for the investment. After contacting us, we investigated the problem and began to act.

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With the tools and professional work of our campaign managers, we increased customer sales by 2.5 times, the number of transactions jumped by 2 times, and the purchase rate (eCommerce rate) increased 4x (!). In addition, the customer paid about 20% less on advertising costs.

This case study speaks only of advertising on Google, not Facebook advertising because Facebook advertising has combined different goals not necessarily increase store sales and/or traffic to the site. But… there, too, there was a significant improvement.

Case Study #1: The Client

For reasons of customer confidentiality and without going into detail. The customer has an e-commerce website where customers can order food products for the home and get it by courier to the home. He is not a large client compared to his competitors and is no known more in the market.

During the period, no change was made to the customer’s website, which could be neutralized by the results obtained by us. In the general calculation of the site (organic and links or referrals), excluding advertising, there was a general increase of 10-20% in sales on the site – a negligible compared with the results.

Case Study #1: The Problem

The customer came to us like other customers, after he advertised on Google and Facebook with a previous advertising company, after the results were not exciting, the customer decided to take everything to his reins. As a result, the client did not manage the campaigns almost because he was busy most of his day – what everyone does and should do.

We were asked to examine the campaigns on Google and Facebook and found many problems:

  • There was no strategy.
  • There was no clear working tactic.
  • Target audiences were neither potential nor always converting (paying) customers.
  • Unorganized structure and hierarchy for account and campaigns.
  • The messages of ads/advertisements were ineffective.
  • Bids do not match the goal.
  • Return on ad spend was very low.
  • We also found that campaigns were run from multiple advertising accounts on the same system (!) – not concurrently.

Case Study #1: The Tools

We used some common tools:

From time to time, we’ve used additional tools to track clicks on ads from competitors, tracking site operations, Dashboards, and more.

Case Study #1: The Challenge

The challenge was to understand how to begin to deal with problems while making effective changes in a relatively short time. The amount of information that the customer had was enormous (too much information): dozens of campaigns, years of activity, factors involved before and more.

It was also difficult to rely on past data, because their performance was not right in the first place and of course it was a long time ago and it is not certain that what worked in the past will work now.

Of course, we had to make a decision of whether to start or correct the existing in accordance with the strategy.

Case Study #1: The Solution

After building a strategy and methods of action we made a decision not to rely on past data and create everything new, from scratch completely. Our experts took the reins into their hands and began applying the solution.

First of all, we checked if Analytics tools are properly installed and if they monitor the real information that customers make on the site. We also created target audiences for remarketing.

Later, we created a new Search Network campaign to bring the maximum number of clicks to a customer’s e-commerce site at a given budget. We have focused target audiences according to the client’s area of ​​activity.

We’ve done keyword research to showing ads based on search terms that customers search on Google. We’ve created ad groups that match our campaign and keyword goals while filtering negative keywords that will not show ads when customers search for them.

We’ve created a variety of relevant ads based on matching ad groups and keywords – delivering the marketing message that’s right for the customer and right for the target audience.

In addition, we’ve created a Display Network campaign to target placements to reach more potential target audiences on the websites they’re browsing. As well as remarketing targeting when the goal was to convey an additional marketing message to customers who were on the website. Ads were matched to the ad group and according to the targeting level.

Of course, during the month changes and updates have been made as we do, including adding/removing keywords and ads, testing A / B, changes and settings, and more.

Case Study #1: The Result

The result after 30 days compared with the previous 30 days was (from Google advertising only):

  • Income / sales increased by 155% (2.5+) – from about $ 1,700 to about $ 4,400.
  • The number of transactions increased by 111% (from 2+) – from 27 transactions to 57 transactions.
  • The conversion rate jumped by 290% (4x) – from 0.15% to 0.59%.
  • Clicks decreased by about 49% – from about 22,000 to about 11,250 clicks.
  • Cost-per-click (CPC) increased by 61% – from $ 0.05 to $ 0.08.
  • The expenditure on advertising decreased by 18% – from about $ 1,100 to about $ 900.
  • Revenue from advertising in the revenue pie – increased by 105%.
  • Return on investment in advertising increased by 210% (3+ times) – from 154% to about 478%.

Although it appears that a decrease in clicks and a rise in cost-per-click (CPC) – are negative metrics compared to the previous month, we need to understand them in depth. The decrease in the number of clicks was due mainly to the filtering of irrelevant customers and the relevant targeting, the allocation of most of the budget to the search network, and even a decrease in the overall advertising budget.

Not only did the customer earn more than 2.5 times (!), but he also paid less money to Google that month. For every $ 1 spent on advertising, the customer earned $ 4.78 in revenue.

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