B2B E-commerce

Increased revenue by 2X
Decreased CPA by 41%

Our client provides top quality B2B products at competitive prices, with the fastest delivery times in the industry. As a highly client-focused business, they offers next business day shipping and fully USA made products to distinguish themselves from their competitors.

B2B E-commerce case study featured image
Vertical B2B E-commerce
Services Search and Shopping

The Performance Proof

Takeover Date: February 2015

 

Challenges & Goals

This client is one of our oldest, so we are extremely proud to have been such an instrumental part of their success and growth. They have a highly specific set of products designed for a niche B2B clientele. Their industry keywords are very competitive and have high CPCs, which together with a longer-than-average sales cycle creates numerous attribution challenges both inside AdWords and holistically.

Approach, Strategy & Solution

Throughout this long-term relationship, we have utilized many strategies as our knowledge and experience with the account has evolved. One particular challenge we have had to deal with repeatedly is attribution, due to a very long sales cycle and multiple touchpoints in the conversion path.

In AdWords we adopted the attribution model of first click for this account, which we don’t often use. However, after running an attribution non-branded data analysis we discovered that first click attribution allowed us to have the most leverage with our non-branded campaigns.

The above tells us the first touchpoint on a non-branded term where the customer is first discovering the brand is far more important than any branded clicks. The most credit should therefore be attributed to when we first get the customer to know their brand at the keyword level. Once we changed from a multi-touchpoint attribution model to first click, we observed significant increases in overall efficiency and sales on non-branded keywords.

This account has gone through a series of very different campaign structures. We have had SQR based structures, which was followed by a funnel structure, which was replaced by our current product category-based structure. Various tests along with years of experience working with the business revealed this as the most efficient structure for the account.

The different categories of the account have a high variance in AOV. For instance, the main displays sold by the client run for over $1000, however their lighting and accessories can be under $100 apiece. Thus we chose to focus on the high AOV sales, while still promoting cheaper items at an efficient customer acquisition rate. This structure has many granular and detail-oriented features. All keywords are carefully sorted by product category, which is a challenge for their business due to the myriad of ways people search for their products. There are also similarities in the search terms of cheaper banner signs and bigger displays or booths.

Our ad groups are extremely granular as well, due to our custom headline one strategy. We want to serve our headline ones as close to the keyword as possible, as this promotes a higher CTR, higher relevancy, and ultimately leads to a higher quality score. A high-quality score leads to cheaper CPCs, which has a dramatic impact on overall performance. We have diligently aligned thousands of keywords with custom ad copy headlines that are both grammatically optimal and contain uniform word ordering, even if the user enters the search terms out of order. A great deal of our keywords in the account have come from converted queries, which tend to be grammatically incorrect. This, however, is our main strategy for long term account growth: analyze converted queries from SQRs on a monthly basis, adding new terms in the account as keywords. This grows the account through natural means, keeping it up-to-date with the latest trends.

Our devices are broken out in a way that gives us optimal control over bids on each device. This particular account sees the best performance on Desktop, which is therefore where we allocate the largest portion of the budget. Maintaining bidding control on each device at the keyword level really helps in this area, as does having full match type coverage. This refers to having every keyword in every match type, which results in the most possible query captures.

Custom bid adjustments for all available segments, such as geo, age, gender, household income, and schedule, play a huge role in the performance of every account, Our client being no different. Thus, we chose to create a custom formula to calculate the appropriate bid adjustment. It is ROAS-based and serves the purpose of calculating the bid adjustment needed in order to get to our goal and keep CVR at its current level. However, this formula needed to adapt with time, while also taking into consideration the current bid adjustment that is in place, giving the most precise mathematical output. We do enjoy incorporating Excel into our processes at Pure Digital, often utilizing it to solve fascinating mathematical challenges.

The final segment of the account we addressed is the extremely detailed negative keyword strategy. We continue to run weekly negatives in order to keep inefficient queries out of the account. Negatives from our exact match keywords are then cross-matched into the other match types, which allows us to selectively see our keywords as queries in our exact match campaigns, while providing better control over bidding on the query level. The query level data is essentially kept cleaner and easier to analyze. This is followed by cross-matching negatives across the different categories, which helps us keep queries going only to the landing pages designated for that product. All of our final URLs are customized to the individual keyword. Combining this strategy with cross-category negative keywords ensures that the user is taken to the right landing page of the product they are searching for every time they click on one of our ads.

Results

Increased revenue by 2X
Decreased CPA by 41%
Increased qualified leads by 25%