PPC Management in Texas

In geography, across every channel at once. Texas metros are large enough that a radius set at metro scale reaches people forty minutes away in traffic who will never convert — and because the click and impression volume looks healthy, the waste is invisible in aggregate reporting. Segmenting existing spend by suburb across search and social simultaneously routinely shows a third or more going to areas that have never produced an enquiry. This is a portfolio-level finding rather than a channel one, because the same misconfiguration usually exists in both channels and neither specialist is looking at the other. It is also diagnosable from existing data in about an afternoon, which makes it the first thing we do rather than something that requires a new campaign to discover.

On measured cost per qualified lead per suburb, once one conversion definition is applied across channels and attribution is deduplicated. A Houston or Dallas business is effectively running several local campaigns, and their performance varies considerably — some suburbs convert well and cheaply, others generate clicks and nothing else, and the difference is frequently not what anyone expected. Channel-level management never surfaces this because each specialist optimises within their channel across the whole metro. The portfolio view reports a grid of suburb by channel, which usually produces a clear reallocation: concentrate on the three or four suburbs that genuinely convert rather than maintaining thin coverage across eight. That also helps the conversion volume problem, since concentrating spend gets campaigns above the threshold bidding needs.

On enquiries and pipeline tracked with your sales team, not on conversion rate alongside consumer campaigns — because at a handful of clicks a month, conventional metrics are statistical noise. Houston industrial and energy queries have tiny volumes attached to enormous contract values, so a portfolio report ranking campaigns by cost per lead will place these last and recommend defunding them, when a single conversion might justify the entire annual budget. The honest approach is to carve them out of the automated comparison and review them separately with sales, tracking named enquiries and pipeline progression rather than rates. It also means manual bidding rather than automated, since there is not enough conversion data for an algorithm to learn from, and saying that plainly avoids a lot of wasted optimisation effort.

Reported separately, because its economics are not comparable. An Austin technology company competes for keywords priced by national advertisers rather than by the Texas market, so its cost per click runs several times what a Houston or San Antonio local service category costs — and including it in a straight cost-per-lead ranking with the other metros makes it look like a failure regardless of how well it is managed. If the same business runs both national Austin campaigns and local Texas campaigns, those are two portfolios with different benchmarks that happen to share an owner. Blending them produces an average that describes neither, and the practical consequence is usually that the national campaign gets cut for underperforming against a benchmark it was never operating under.