Ads Don’t Always Sell What They Are Created To Sell
Your ads are often not selling the products that you designed them to sell. We have found that upwards of 48.38% percent of the time people end up buying a product different than the the one featured on ad they clicked on, and upwards of 11.28% of the time they bought a similar product, but […]
Your ads are often not selling the products that you designed them to sell. We have found that upwards of 48.38% percent of the time people end up buying a product different than the the one featured on ad they clicked on, and upwards of 11.28% of the time they bought a similar product, but not exactly the same product as the ad they clicked on.
When conducting this analysis, we excluded keywords that were brand terms and general category terms. For example, if we were analyzing a site that sold camping supplies and someone did a search for “camping supplies,” we did not include that type of keyword in this analysis because any product that was sold from the site was a camping supply. Instead, we focused on keywords that were product names, derivatives of product names or about a specific type of product.
For each keyword and product combination we looked at, we placed them in to one of three buckets: Exact, similar and unrelated. “Exact” would mean they purchased exactly the same product or product category of the ad they clicked on. “Similar” would be if they looked for XYZ sleeping bag, but purchased ABC sleeping bag, meaning they still purchased a sleeping bag, but it was a different brand or model from their search. “Unrelated” represented when the users search had no relation to what they ultimately purchased.
After conducting this analysis, we found the following information:
The benefits of this analysis are many, especially when it is available as part of your advertising analytics solution. First and foremost is the accuracy that comes with understanding which products are sold by each ad. This insight allows companies to apply real margins to each conversion vs. an average margin. This accuracy ensures that clients never overvalue or undervalue the contribution an ad delivers. Given that bid prices are set and budgets are allocated based on the metrics we see in our advertising, it is imperative that these decisions are made on accurate data to ensure we are getting the most out of our ad budgets.
In addition to the accuracy of performance metrics, this analysis can also be used to improve the performance of ads by identifying business actions that can be taken. For example, one of our clients had a set of keywords designed to sell product A, and we found that 90% of the time they sold something other than product A. This discovery was brought to our client”s attention. Our client did an analysis of their own business to see if they could understand why people looking for product A chose to buy a different product. They looked at the price of product A relative to the price the competition was selling it for. They also looked at their selection, shipping, return policy, etc., on that product relative to their competition and determined they had the most expensive price for that category of products relative to their competition, and that some of their competiton offered a much better return policy. Based on these findings, they brought their pricing in line with the competition and changed their return policy. Now, that keyword sells what it is intended to sell 40% of the time versus the 10% rate before they had this information.
A lot of times we blame our advertising for the lack of conversions or lack of profit it produces. An ad”s job is solely to bring the right prospective customers to your site. It is your site”s job to then convert those prospective customers. This analysis shows how a group of prospects that are looking for certain products may never end up buying them. This also forces the advertiser to ask the question “Why?” The “why” very often has to do with your price, selection, tax and shipping, site navigation, checkout, inventory, and perceived credibility. If you always blame your ads without looking at these other factors, you will waste a lot of money and time manipulating your ads only to end up with the same results.
A deeper dive into this type of analysis can help marketers identify potential up-sell and cross-sell opportunities. For example, if you discover that a lot of people who search for peanut butter and buy peanut butter also buy jelly, you could target any customer going forward that has purchased peanut butter or jelly with an offer to buy the other product. You may also rearrange your site so products that are often bought in tandem are shown together to increase the up-sell opportunities. Another tactic marketers can use is to send custom email messages to their customers that offer them products that are either related to what they actually purchased or are for the product they originally looked for, but chose not to buy.
This information may come as a surprise to many marketers. “How can someone that is searching for one product end up buying something very different?” The reason why people do this is interesting, but not as important as knowing that it does happen. If you have the ability to see which products are actually sold as a result of an ad, then you have a major opportunity to improve not only the performance of your ads, but the performance of your overall business.
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