At Rosetta, we address many challenges that our clients encounter in trying to gain the best results through search marketing. These challenges consist of tackling the emerging mobile medium, seeking co-op funding from vendor partners or, in many cases, optimizing search to eliminate conflicts and bidding wars between two different product groups or channels of a single company. These conflicts ultimately create inefficiencies and increase costs of marketing two or more similar offerings – leading to inherently higher acquisition costs and a degraded operating margin.
To illustrate this, here is an example: ACME Corporation sells and services widgets. They sell widgets through their distribution channel, but market the product via the company home page. They also offer world-class services through a separate site. The company home page and the associated search marketing investment is managed by a vendor partner working with ACME’s marketing group, and the services site search is managed internally. Without a clear corporate direction or combined strategy both groups compete with each other on keywords, create search engine confusion on priorities, etc. This leads to extra cost in the paid search area and minimizes potential opportunity.
These internal conflicts can be mitigated using several strategies. One way is to delineate the boundaries between the competing channels, dictating keyword selections for each. Real-time testing may reveal one channel performing more strongly for a product line, therefore securing exclusive rights to the related keywords. However, the best strategy is to manage the search investment at a corporate level, making sure the funds are managed efficiently and the opportunity to maximize results is realized.
To help our clients resolve these internal conflicts, Rosetta has developed an approach that helps companies determine the best course of action. Through proprietary forecasting tools Rosetta can valuate the revenue potential of a search program: paid, organic, or both. By understanding the search revenue capabilities of brands/channels, clients can easily work backwards to allocate appropriate funding levels to each. These funding levels would then dictate keyword selections, as higher value programs could afford higher funnel keywords and support of categories and product lines synonymous with higher costs.
The result is a data-driven decision process that allocates optimal funding levels to each brand that, in turn, assists in resolving overlap and search engine conflict. Forecasting a brand’s search revenue also aids in media mix decisions to further optimize marketing spend across tactics, thus creating a more balanced, holistic approach to the online channel.