Appcast White Paper: Programmatic Ad Buying – A Game-Changing Strategy to Increase Candidate Traﬃc for Your Job Site. To read the full whitepaper from Appcast, click here to access.
Every market for which job sites are a predominant source of matching jobs to candidates eventually ﬁnds that demand for candidates outstrips what can be achieved organically. This is especially true when unemployment is at a historically low rate. To bring more candidates into the funnel, most job sites start with a branding approach that rapidly evolves to include Google Ads, standard display advertising and the re-syndication of jobs to secondary job sites. Generally, the cost-per-acquisition of new registrations, candidates and applicants is signiﬁcantly lower via re-syndication than any other method of acquisition. Few job sites can overlook this cost disparity in their decisioning around how to spend a limited budget.
In the North American and European markets, there are numerous buyers and sellers of candidate traﬃc. While some job sites do both in a true arbitrage model, most ﬁnd themselves either primarily client facing with a strong candidate brand and a large direct sales organization (but still not enough candidate traﬃc to meet their clients’ demands) or candidate facing with a small sales organization focused on generating and selling candidate traﬃc to larger job sites. A wholesale market thrives in this environment, allowing for lower per-click pricing than in the retail market which generally supports those large sales teams, infrastructure and signiﬁcant branding eﬀorts.
Whatever the needs of your job site, one fact is clear: without constant management, traﬃc providers will spend your money but not necessarily eﬃciently, eﬀectively or with the quality candidate traﬃc clients demand. These goals are only reached through a programmatic approach.
Where does the current model fall short?
Currently, job sites (buyers) manage the process of acquiring traﬃc with their own development resources. The process typically involves development teams generating and maintaining XML ﬁle feeds for each vendor that you are acquiring candidates from, ﬂat rate per-click pricing (or feed-level pricing if you have a more sophisticated operation), incredible spreadsheets, a little Excel wizardry, and weekly or monthly updates on spend.
At the end of each month, the buyer will invest the next week or so combing through the spreadsheets to understand what is actually happening on the ground. Are your candidate traﬃc sources meeting volume expectations? Is that volume converting into registrations and applies? What is the conversion rate and price-per-lead? What is the price-per-apply or price-per-apply-click for your customers? Are they providing candidates evenly against all your jobs or is traﬃc highly concentrated?
As you can see, this model is frustratingly labor intensive. It also has some serious structural problems:
Tough to control over- and underspend
The only way to control for overspend is to cap your publishers with a predetermined budget. Only now, every decision becomes a rear view mirror. You can’t easily identify and adjust mid-swing if your publisher is delivering more traﬃc than expected, and you can’t quickly reallocate the budget if your publisher underdelivers.
Delayed reaction to changing conversion rates
With manual traﬃc-buying programs, the feedback loop to your candidates’ sources has tremendous latency. With this latency, publishers can send an overwhelming amount of traﬃc that accumulates “clicks” – meaning you pay for it – but doesn’t convert into the required volume of lead registrations or applies. If your candidate source’s conversion rate drops suddenly, you will not know until the end of the month. In the meantime, the bad traﬃc is consuming your budget. Conversely, if a candidate source conversion rate suddenly spikes, you cannot capitalize, in real time, on the high-quality traﬃc.
No control over traﬃc quality
Changes at the candidate source level can impact the expectation in terms of quality. For example, publishers might introduce new matching technology, add new re-syndication partners, or innovate new methods for engaging the audience such as text, mobile apps and mobile applies. Changes in the candidate source’s traﬃc acquisition strategy changes the expectation but if you have no real-time analytics, you cannot capitalize on this.
High dependency on your development team
Besides the requirement to build initial feeds for each new publisher, you will also need your development team’s assistance to:
• All requests take time, and sometimes a lot of time. It’s not uncommon for job sites to experience a catastrophic drop in candidate traﬃc while waiting for these resources.
• Add new publishers
• Split feeds to facilitate feed-level pricing
• Identify and track down broken feeds
• Establish the root cause of broken feeds and ﬁx the problem
All requests take time, and sometimes a lot of time. It’s not uncommon for job sites to experience a catastrophic drop in candidate traﬃc while waiting for these resources.
The challenging online ecosystem
As with standard job ads, the current traﬃc acquisition model fuels the misconception that every job and every applicant is equal. In fact, when you are advertising jobs in the online ecosystem, it quickly becomes apparent that only a portion of jobs will perform in terms of delivering the right volume of clicks to get to a candidate conversion. Market data from Appcast suggests that:
Your publishers are motivated by their monetization models to drive a lot of clicks. But if those clicks are not converting, and many of them won’t as the data shows, they end up being a waste of money. Poor converting jobs can consume as much as 10% of the budget. Unsurprisingly, the idea that your budget – thousands, hundreds of thousands or millions of dollars a month – is controlled by publishers whose motivations are misaligned with your own does not sit well with stakeholders.