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About the Client
Hanes Brand is an American casual clothing manufacturer with an omnichannel distribution model. The main source of sales for the company is the retail business with a network of offline points and its own online store.
The B2B direction, where Hanes, under the sub-brand Alternative Apparel, supplies corporate clients in the USA and globally with basic clothing, also occupies a significant place in the existing business model.
Our team participated in the project as a subcontractor for the New York studio Adventure PPC, where we were set common goals for the development of the B2B direction of the Hanes sub-brand - Alternative Apparel with the following tasks:
Increase the number of B2B leads in the form of corporate orders from existing and new clients.
Differentiate B2B and B2C audiences. It was important to ensure that advertising for wholesale buyers would not overlap or compete with campaigns aimed at the retail segment.
Increase revenue. The final assessment of the quality of our work was the increase in revenue of the business unit not only in the form of direct orders generated by advertising but also in general across the branded and direct sales channels of the division.
Challenges
Avoiding the overlap of retail and corporate client audiences while promoting wholesale prices and offers is one of the most difficult tasks, as this can cause a negative reaction from the main layer of B2C buyers.
Despite the fact that in Google Ads we could use such B2B queries as screen printing (screen printing on clothing - this is what corporate clients look for in 95% of cases), up to 90% of key phrases were unavailable to us at the start. This happened precisely because of the specifics, as the "decision-makers" constructed (expressed) search queries when searching for goods in the same way as retail buyers.
We conducted an analysis and compiled a digital portrait of the decision-makers on the corporate client side of the business, dividing them into 18 main segments. For each designated group, more than 20 triggers were identified that helped to isolate the necessary people into separate audiences. Examples of such determining factors were:
- the most visited websites related to their work;
- key phrases and queries they might enter in Google search when solving various business problems;
- a list of the most common suppliers and their websites that the target audience might use;
- other factors.
Setting up Narrowly Targeted Promotion in Search
Advertising campaigns targeted at "fine-tuned audiences" were created for the target search queries that we could not use before. This allowed us to minimize the overlap of retail and wholesale buyers.
Additionally, Search Remarketing was set up for the selected groups, with which we actively interacted with consumers who, having visited the B2C online store, fell under the profile of "decision-makers" among wholesale buyers
Targeting
We paid special attention to targeting by ad display time and geo-parameters. Thanks to this, we managed not to dissipate the already limited budget.
Display Time. For this, the most active time for purchasers among potential buyers was determined with the help of the Alternative Apparel sales team. Using both the company's internal analytics and special surveys of existing wholesale clients of the business, we found out on which days and hours maximum activity should be ensured for key search queries.
Geo-Targeting. We have a database of potential customers in all key regions of operation. Using a specially developed machine learning algorithm, the geodata of the location of client company offices was formed. Thanks to the aggregated conclusions, we transmitted information about geographical targeting and the range of action of specific marketing communications to Google Ads, Bing Ads, and Facebook Ads for separate advertising campaigns using API integration.
Unique banner and video advertising campaigns were created for each audience with specially selected creatives and material design that evoke an associative reaction using trigger elements.
For this, we used images of sets of the most popular clothing kits, characters associated with employees (couriers, builders, workers, etc.), as well as several versions of visually highlighted "mock-ups" of logos for which the font of "clichéd" and classic forms, most often used by local businesses for logos, was used.
It was precisely this selection of formats and elements that allowed us to receive the most relevant clicks from the selected audiences, which, despite their simplicity, proved their superiority over fancy creatives during split testing.
Ad Chains
Potential buyers saw a series of banners and video ads in a specially constructed order, where every user was shown a completely new creative with a new message every 7 days, segmenting and separating over time those who did not interact with our advertising materials within the first 30 days or the first 10 impressions.
Similar Audiences
In addition to transmitting information about orders from the CRM to web analytics services in the form of conversions and transactions, we uploaded contacts of both existing buyers and interested potential clients who were marked by the team as quality leads.
Lookalike audiences were created in Google and Facebook Ads based on these lists, which allowed us to literally train the existing system within a month and, together with the previously set up "fine-tuned segments," create the most accurate targeting for the desired potential customers.
Automated Strategies
After two months of active experiments, manual control, and optimization of advertising campaigns, we switched to automated bidding and display strategies, based on conversions into real orders and revenue information, received and transmitted to the advertising accounts using end-to-end analytics.
Thanks to the correct training of the system with accurate data and manual optimization, we were able to obtain 46% more quality leads for the same budget than at the project launch.
The Role of End-to-End Analytics
This was the foundation of success. Initially, the correct synchronization of data from the business's ERP system, Google Analytics, and advertising accounts allowed us to assign and transmit a unique ID to each client and their key actions, fixing the client across different devices, browsers, under different Google Client_IDs, and communication methods with the sales department.
This made it possible, with a large amount of data, to better train and automate the work of Google and Facebook algorithms, focusing not on clicks and applications, but on payments and real revenue as the target conversion, evaluating all promotion channels as a single multichannel sales mechanism.
Brand Queries and Competitor Audience. Thanks to compiling a list of profile competitors working only in the B2B niche, we obtained one of the most profitable audiences in the project.
Ad Extensions. Our teams approached the issue of creating extensions in Google Ads search ads unconventionally for the corporate casual clothing sales market, highlighting aspects that concern only wholesale buyers as aggressively as possible. Thereby, we achieved a reduction in B2C traffic from 28% at the start to 7% after 6 months of promotion.
Cascading Remarketing. Considering the quite long purchase cycle (negotiations, fabric selection, models, mock-up approval, receiving the first trial versions), up to 30% of deals in Alternative Apparel were previously considered lost.
Using a chain of remarketing communications, we kept all clients in focus until the moment of the first payments, changing banners, messages, and incentives for each buyer, depending on the stage they were at, receiving information about this from the CRM database. As a result, the percentage of lost deals was reduced from 30% to 22% within 8 months of the project's work.
Achievements
+166%
Revenue growth in the B2B direction through paid channels
+64%
Increase in yield in the ratio of revenue to promotion costs.
54
Advertising campaigns for 18 audiences at the acquisition and retention level.
+111%
Revenue growth from brand traffic and direct queries.