The Emergence & Impact of Programmatic Advertising on Recruiting
THE INTERNET IS THE JOB BOARD
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Pioneering “Programmatic” Since 2008Across the Largest Recruitment Ad Network in North America
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over 600
125,000
Powering
Publisher Sites
Used By Over
Employers
526k+ProgrammaticallyJobs Campaigned
78 MillionResulting in over
Job Matches
Our Programmatic Recruitment Advertising PlatformConnects Employers and Job Seekers Across the Web
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Job Site AutomatedAd Distribution
Predictive Analytics
Software
Job MatchingAlgorithms
The automated buying, placement and optimization of ads through software, not
people
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What is Programmatic Advertising?
Programmatic = Automated
Presenter
Presentation Notes
Lets first define What Programmatic Advertising is. Think of programmatic simply as automation. It’s the use of software for buying placing and optimizing ads instead of people Notice the definition does not require a type of ad (print vs. digital) nor how the ad is measured whether it be based on duration, cost per click or cost per Application, all of whch could be programmatically campaigned. So with that definition we are going to have our first audience Poll to see what level of expertise we have in programmatic campaigns. As we run that, lets look at how market adoption is occurring for programmatic advertising, first with consumer display ad marketing.
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Programmatic Advertising Adoption
Will adoption in recruitment follow the Consumer Market?
Presenter
Presentation Notes
If we look at adoption in the consumer world you can see that in the last 5 years, programmatic consumption of ad spending has gone from 45% in 2011 to an estimated 79% in 2016. Here programmatic is further broken down between non-real time bidding and actual RTB, which is now the dominate form of advertising. So how does this adoption curve apply to recruitment? If we assume the same adoption model and timeframe, than….
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By 2020 80% of all recruitment
advertising will be based on programmatic campaigns
using Big Data
Presenter
Presentation Notes
While we are still at the very early adopter stage, programmatic is growing at tripe digit rates. think about that for a minute. What does that mean for you business, if the process by which the buying, placement and optimization of job postings is fully software optimized. What's the impact of cost reduction, resources, increased margins for your business. Lets get a little more specific on what programmatic means to recruitment advertising.
What should Programmatic Recruitment Advertising Deliver?
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Job distribution to all sitesDynamic control of job visibility and performance Ability to target a specific audience Accurate prediction of results as compared to the marketEfficient spend Transparency on spend & performance of each job
Presenter
Presentation Notes
Programmatic recruitment advertising should deliver the following capabilities:� - Job Distribution to all Sites (not just a select few aggregators, but the reach should be across the internet including social sites, diversity sites, etc.)� - Dynamic control of job visibility and performance (this is where RTB comes into play) If you have the wrong bid on an aggregator site, your job does not get sponsored and it just shows up only in organic job inventory and it does not get seen and delivers no performance. How often do you need to submit a new price and at what increase do you need to ensure you get performance? Now its starting to get complicated. - Ability to target a specific audience. This includes candidate Demographics, geographic location and importantly for recruitment (behavioral targeting indicting the level of engagement such as a passive candidate and candidate qualification) Programmatic should also provide an Accurate Prediction of what the results will be as compared to the market (taking into account the competitive nature of the job, such as how the job posting competes with other postings for the same kind of job and the supply & demand of candidates for that job opening) now its starting to get really complex… - Efficient Spend (stop paying for clicks and focus on applying the spend to target and attract the most qualified applicants) And Finally, it should provide Transparency on the Spend & Performance of each job If you can do all that, than programmatic will deliver a far better ROI than the current process of job advertising process, which if is not automated, it simply cant deliver all these capabilities. So if you think recruitment advertising is complex and specialized, lets take a look at the specialization that has occurred in the consumer Ad tech world.
In case you thought HR Tech was Specialized!The Consumer Advertising Programmatic Supply Chain
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Presenter
Presentation Notes
Here is the supply chain for delivering if an ad placed from a marketer all the way to the publisher and ultimately the consumer. You need a magnifying glass just to see all the hundreds of vendors and the specific and different subcategories of providers. Image the change in our industry if RecTech is to get as specialized as Adtech How is one to navigate this supply chain? At RealMatch, we think the answer to navigation of recruitment advertising is Predictive Analytics. Lets talk about how Predictive Analytics applies to Programmatic Advertising.
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“Predictive Analytics gives decision makers the power to‘see around corners’.”
–Tom SiebelFounder, Siebel Systems
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Presentation Notes
I think Tom Siebel described it well, saying “Predictive Analytics gives decision makers the power to ‘see around corners’.” insight into things we couldn’t otherwise see on our own. I don’t know how many of you have ever been in a self-driving car? It’s a surreal experience. Your first instinct is to lunge for the steering wheel at every turn. But very soon you come to understand that the car really does the thinking and driving for you. Cruise sensors automatically monitor highway markings and traffic, constantly adjusting the steering to keep the car centered in its lane. After a little while you begin to shift your focus from the car and the steering wheel to what you can now do as you are hands fee and no longer worried about the steering. Things like use his phone, read a book or just enjoy the view. Well using that self driving car analog, If Programmatic advertising is a self driving car, than Predictive Analytics are the sensors that control the navigation (the steering wheel if you like). These sensors enable us to see blind spots we wouldn’t otherwise see and to automatically respond. Even enabling us to see around the corners. So if we have the right sensors in place, how does predictive analytics enabling us to drive recruitment better?
Predictive Analytics Drive the 3 most important Talent Acquisition Indicators
So, what Indicators do Recruiters use for Talent Acquisition?
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Cost Quality Time
Presenter
Presentation Notes
What sensors or indicators do recruiters use to manage Talent acquisition? Just because we have a new automated way of managing the sourcing process, the indicators or dashboard criteria hasn’t changed. Those Key performance indicators have remained constant and they include the Cost of candidate acquisition, the Time to fill and the Quality of candidates. We can understand how giving up the steering wheel to automation is necessary for adoption, but its also a little scary. Using the self driving car analogy, if an automated approach doesn’t get the passenger to where they want to go, safely, and at the right cost, then it just wont get adopted. Likewise If programmatic recruitment advertising is going to be adopted, we need to show that it can have a meaningful and significant impact on all three of these key indicators. Our goal today is to show you how an effective campaign navigation system using predictive analytics can drive these KPI’s by eliminating the blind spots we have in our current navigation system.
Driving Cost Efficiency
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Presenter
Presentation Notes
Lets start with Cost and see how predictive analytics can drive cost efficiencies by providing sensors that can help us better navigate and eliminate blind spots.
In Consumer Advertising…..
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“70% of advertisers agree that cost reduction is a top benefit of programmatic ad buying” – Ad Age
Presenter
Presentation Notes
We like to compare recruitment programmatic advertising to our predecessor in consumer advertising. According to ad age, 70% of advertisers agree that cost reduction is a top benefit of programmatic. #1 Problem in PPC is uneven distribution of campaign budget across jobs which results in too many click on one job and not enough clicks on another others that need it. We call that a blind spot. Platforms that drive the best performance will capture a larger percent of the ad budget. So there is a high degree of focus on cost savings. However, using our car analogy our current state of programmatic has a blind spot when it comes to managing cost. Arbitrage (the most prevalent pricing practice in our industry right now) is a short term strategy that is not a viable business model and sustainable practice. Buying low and selling high doesn’t work when advertisers want visibility into the sources and cost of sources. Putting job inventory as backfill is akin to selling remnant inventory Its a race to the bottom for impressions, which is not sustainable As the Market Matures it is increasing priority from Cost to Quality, using creative content and targeted techniques
Today’s Methodology: Campaign Trial and ErrorAllocate a set budget against jobs Set limits to stop spending per jobStop spending on sources that deliver low CTR Evenly distribute budget across all jobs until you run out of budgetGet more money and establish a bench mark for future campaigns
Performance visibility at the job level makes it possible to be proactive in spend optimization and to predict the right bid from the start.
Presenter
Presentation Notes
The first blind spot is the inability to predict & optimize Bids on a job specific basis. If the only data we have is what's in the rear view mirror, and not what's in front of us, then our navigation system is not going to get us very far. Just as self driving cars are slowly being introduced to the populace, So is programmatic advertising. We’ve kind of taken a page out of the automobile industry when it comes to adoption. Automobile manufacturers have slowly introduced self driving capabilities. How many of you have a car that can parallel park on its own? Most new cars have that now built-in. While parallel parking may be hard for a lot of us, (I have a 16 year old getting his driver license and I saved training that part for last.) but from a programmatic standpoint it really is the easiest use case. It’s a very controlled scenario with few variables (low speed, only 2 other cars at best to worry about, and they are not moving and there is not much space to navigate. Low margin of error and low risk of major damage. ) the same is true for programmatic recruitment advertising. Right now we have a very controlled methodology, a pretty simple use case with not a lot of variables, which primarily focuses on when to stop spending. It’s a trial and error approach on a campaign level. We set a budget, set limits on spending and we stop spending on traffic sources that done deliver at minimum levels. That gives us the ability to take money that would have been effectively wasted and spread it across jobs that need additional spend. We try to spread the budget more effectively across all the jobs. That is management at a campaign level. That’s a good start. That’s like parallel parking. But campaign management at an individual job level is much more complex and requires a lot more variables. More like navigating on a highway at 60 mph. Performance visibility at the job level makes it possible to be proactive in spend optimization and to predict the right bid from the start as opposed to using latent data from our rear view mirror to try to see ahead of the curve. If no job specific criteria is set before the spend, then much of the spend is waste and used primarily to experiment or establish a benchmark for future spend Initial bid amounts are usually a best guess or simply whatever budget you have, or are willing to try. Today, they have little correlation to what’s possible or an expected outcome. has the Biggest Impact on Cost Savings
What if I had a Guidance System to Predict the Right Budget for each Job from the Start?
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The ‘distance’ between the optimal prediction and the actual performance at any given time requires real-time tracking of many performance related variables.
Predictive Analytics Enables Cost Savings on a Per Job Basis
Presenter
Presentation Notes
What if I had a guidance system to predict the right budget for each job from the start. How much waste would be eliminated and how much performance would be gained We’ve all have used GPS systems like Google maps or Waze to get from point A to point B. A good guidance system will always be calculating the distance between the optimal patch and the actual location which at any given time requires the tracking of many, many performance related variables, like traffic, weather, accidents, speed, etc.� So it is with job level campaigning, predictive analytics enables us to determine the correct bid at which to start and then monitors the performance to adjust as necessary and ensure we get there with the right results and at the right cost. Instead of spreading your campaign budget around to different jobs, in a trial and error process, we are going to predict the right cost for each individual job and then manage the performance to that prediction.
The Key to Good Predictive Analytics is Use of Big Data
What’s Needed in Order to be Able to Predict Job Performance
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Determine which job parameters effect performance
Capture performance data across hundreds of thousands of jobs
Use machine learning to analyze, inform and improve the model
Run each new job against the model to get performance prediction
Dynamically adjust job campaign after each view to manage results to the prediction
It’s not just about looking at the performance of an ad buy. It’s more about understanding why that buy performed so well. What worked best?
Presenter
Presentation Notes
What data do we need for Job specific prediction? the more data you have, the more visibility and the smarter decisions you can make. First step is to determine what job parameters effect performance from job type to job ad format, logo or no company logo, Apply mechanism, platform optimization (mobile vs. desktop) supply & demand, etc. employment type, etc. We built a model of 50 different job specific parameters which are the most influential when it comes to determing performance use machine learning to build an accurate model Run each new job against the model to get performance prediction Dynamically adjust job campaign after each view to manage results to the prediction But we have found 2 variables that have the most impact on performance, and its not surprising which ones they are.
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Which Data Parameters are Most Important in Predicting Job Level Performance?
Lets start by looking at just two variables:Job Title & Location
Presenter
Presentation Notes
Prediction is based on historical data, the more historical data we have, the better the accuracy. The two most influential parameters in the prediction model are the job title and the location.
Job Title & LocationHow Big a Data Problem is Managing Job Title and Location for performance?
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• Add in related key words for searchIdentifying similar job titles is key to expanding search results.
50,000 + 10,000 X 65,000 = 3.9 Billion performance data points required
10,000+ Job Titles X 65,000 Cities in US= 650 M Performance data points
Presenter
Presentation Notes
For prediction we need a lot of historical data points on performance This means we need historical data for 2 Billion data points. If a job title comes in with a title and location that has not established a performance benchmark, than we are running blind and we have to go through trial and error and we cant manage the job to a desired result. Think of each job as a cube, a really big cube, that can hold 3.9M data points. now think about capturing the data to populate every cell of that cube so you can accurately predict. How long would it take in running jobs to populate all the data. The answer is a long, long time. Too much time to be practical.so we came up with a shortcut.
Eliminating the Predictive Performance Gap
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How do you ensure you have performance data for each unique job?
Build a hierarchical taxonomy of job types, key word synonyms, skills, education type, engagement (full time or part time), industry and detection of non-human traffic, etc., etc., etc.,
220 X 65,000 = 14.3 Million data points
Presenter
Presentation Notes
by categorizing jobs to the a hierarchical taxonomy, we are able to reduce the amount of combinations required to have historical data for each unique combination of job title and location. Our ability to reduce the options to 14.3 M enables us to predict better and faster. Fewer combinations means better prediction and no historical performance gaps. More data for each data point makes better prediction. hierarchy enables prediction on sub field levels such as skills required in a job posting,. Probability of having data for these combinations increases and enables performance prediction on a job level. This works equally well for esoteric job titles as well as simple or popular jobs
Hierarchical Taxonomy of Job Titles & Related Synonyms
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Presenter
Presentation Notes
For a finance clerk we have 25 related job titles, including obvious ones such as accounting clerk, bookkeeping clerk, etc. and some not so obvious ones including payment processor For a data entry clerk we might use the keyword data encoder When we campaign the job to different sites we append the job posting with the related keywords and synonyms to ensure that when job seekers look for those terms, we are matched. It not only improves our response rate but if a unique job title is processed through our system for which we don’t have historical performance, we can map that job to a related cell in the cube and ensure a performance prediction. Each job which enters to our system is checked against all those taxonomy nodes. An average job is positively assigned about 20 of these variables. For resume we split the resume to its parts: previous experience, education, skills and personal information for each experience we detect job title and skills and on average we detect abound 100 nodes from the taxonomy. When we do our data training to build a predictive model we add to those some additional data like: affiliate id, posting days, job application type, CM budget, company name exists, logo exists, company description exists and so on. Our taxonomy identifies the variables that are required by the Machine learning technology that generates the prediction to maximize the Impression click through rate. We now have 10 years of data collection on the performance of specific jobs. So we’ve filled in the cube. For a finance clerk we have 25 related job titles, including obvious ones such as accounting clerk, bookkeeping clerk, etc. and some not so obvious ones including payment processor For a data entry clerk we might use the keyword data encoder When we campaign the job to different sites we append the job posting with the related keywords and synonyms to ensure that when job seekers look for those terms, we are matched. It not only improves our response rate but if a unique job title is processed through our system for which we don’t have historical performance, we can map that job to a related cell in the cube and ensure a performance prediction.
What if I don’t Have a Job level Performance Prediction?
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Staff Accountant
Job ID #1 – 13892435
Same Employer Name & branding
Same Location: Greensboro, NC
Similar job Description: See note
Same Budget
Posted on the Same Day for 30 days
Accountant/Bookkeeper
Job ID #1 – 13892435
Same Employer Name & branding
Same Location: Greensboro, NC
Similar job Description: See note
Same Budget
Posted on the Same Day for 30 days
Staff Accountant
Job ID #1 – 13892435
Same Employer Name & branding
Same Location: Greensboro, NC
Similar job Description: See note
Same Budget
Posted on the Same Day for 30 days
Running Blind example: No use of Taxonomy
Presenter
Presentation Notes
Point is this is what happens when you don’t have prediction – running blind with prediction we could have redirected traffic to one job vs the other. It did not include use of taxonomy. Why the different results? Same bid amount. Same distribution sites. market demand changes What do you think was the variance in # of views between these two jobs. 10%, 20% 50%. Which has the better result? # of views is meaningless. 13892435 Curry, Ireland & Co., LLP is a public accounting firm seeking a staff accountant to handle the following functions: preparation of Individual, Partnership, Corporate and Trust tax returns light tax research projects� perform review and compilation engagements with minimal oversight prefer at least 2-4 years of recent public accounting exp. undergraduate or graduate degree in accounting, and/or CPA strong client interaction skills excellent organization and time management skills working knowledge of Quickbooks, UltraTax and Microsoft software� � Please email resume, cover letter and salary requirements to [email protected] 15048868 Accountant / Bookkeeper� CPA firm, Curry, Ireland & Co., L.L.P. seeking staff accountant / bookkeeper. CPA not reqd. Exp. preparing individual / corporate tax returns & monthly bookkeeping reqd. Email [email protected] your resume, cover letter, references, software exp and salary requirement. We can see them in the dashboard but without prediction data Those are true different jobs from the same company
Wildly Different Results!
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Accountant/BookkeeperJob ID #1 – 13892435
Same Employer Name & branding
Same Location: Greensboro, NC
Similar job Description: See note
Same Budget
Posted on the Same Day for 30 days
Staff AccountantJob ID #1 – 13892435
Same Employer Name & branding
Same Location: Greensboro, NC
Similar job Description: See note
Same Budget
Posted on the Same Day for 30 days
Performance Results:
181 views 512 views
Presenter
Presentation Notes
What if I told you that The job with the word bookkeeper job got nearly 3 times as many views as the accountant job. Why the wildly different results with every other part of the job campaign being identical? Same bid amount. Same distribution sites. The market demand at that time favored the key word Bookkeeper as job seekers search more for that job title? This shows the importance of having a taxonomy that can categorize a job and identify synonyms for the job title. So a single posting will be maximized for optimal search results. When you don’t have a prediction model you are really running blind because small variations in the posting can generate widely different results. Lets look at the same job, but tis time using our taxonomy and prediction engine. We can see them in the dashboard but without prediction data Those are true different jobs from the same company
aggregated avg clicks aggregated job 2 By % 2Prediction
Optimal Results and CPC Campaign
Presenter
Presentation Notes
With a prediction engine we targeted 138 views as the optimal result and then we dynamically managed the campaign to that result. Can reproduce the results. In addition the prediction matrix can triangulate the expected performance for all the related job titles and keywords. Green line shows the average of views for a partner site with that job title. orange line shows the prediction. And Blue line shows a real life job and how it changes according to the week days. Clear view of Monday Tuesday on day 5 and 6. Orange line shows prediction line and how after 30 days it converges with the actual views. Notice how the First five days gets most of the traffic and how after 30 days it converges with the actual views. Knowing what you have learned about programmatic job campaigns thus far, lets take a poll . (#2) We can predict the performance with 20% accuracy.
Driving Time Efficiency
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Presenter
Presentation Notes
So we looked at how having a taxonomy and performance data can help us provide a performance prediction on a job level. Now lets look at driving the second major indicator for recruiters time efficiency.
#2 Blind SpotOptimizing Campaigns for Time Efficiency
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Time-related Issues:
If 10% of traffic is achieved on day one, then how do you ensure you have the right bid on the first day to maximize the results as quickly as possible?
If Bid and job performance is too low after one week, can you make up for lost time and at what expense?
Presenter
Presentation Notes
Lets look at the blind spot when it comes to managing campaigns for time efficiency. � There are two time related issues with current campaign methodology; We know that on average 10% of a campaigns total traffic will occur on day one. This is due to a # of factors one of which is that we know vendors promote new jobs on the first two days even in organic so that is where they get the most bang for their buck. You’ll usually get a couple of days of additional exposure. If that’s the case, than how do you ensure you have the right bid on the first day to maximize the results as quickly as possible. What if your ad is dark on those two days and not seen do to a low bid? And secondly what do you do if your job performance is too low after one week, Can you make up for lost time and if so at what expense? � Lets look at some things you can do wrt to timing efficiency.
The Weekend EffectTiming Bids for Maximum Effect
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Presenter
Presentation Notes
The first timing consideration is what we call the weekend effect. There is a cyclical nature to job search, so one easy thing to do is to time the bid for maximum results around the week end. Here are 4 performance indicators tracked based on the day of the week. Job Views, apply clicks and completed application all have the same pattern. I want to show you one job example to demonstrate the impact a good prediction can do to take advantage of job performance. You can see Sunday, Monday, & Tuesday all have the highest average return. So what does this tell you: Getting your bid right on Sunday is crucial tomaximizing your performance. Top right is interesting. It showsjob seekers spend more time on jobs on Wednesday. I really don’t know what drives this? Maybe its just hump day and the grind of the work week has already gotten to them. But by Thursday, they can see the wknd coming and they don’t finish the application. I don’t know? But I do know that having the right bid amount out of the gate will better leverage the timing effect and produce better results
A “Process Operator” Job Example What is the Optimal Job Title?
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Process Operator at La Porte, TX
Total is offering employment opportunitiesfor Operations Personnel at its La PortePolypropylene Plant in La Porte,TX and Bayport Polyethylene Plant inPasadena, TX.
Candidates will be required to successfullycomplete and pass extensive testing,drug/alcohol screen, background check,physical requirement evaluation and thecompany's Operator Training Program andprobationary period. Shift work andovertime will be required.
Minimum Requirements60 College Credit Hours (preferably inProcess Operations Technology) as of May31, 2016.Or Five years recent PetrochemicalIndustry experience (preferably in
operations)Or Four years of military experience…
Job Title: Chemical Operator
Skills: Military Experience
Previous Experience: Operations
Education: Some College/University
Job Type: Shift work
Data Extraction & Taxonomy
Presenter
Presentation Notes
Grouping job title to sub fields to eliminate the empty matrix effect Taxonomy helped this job in the following ways: 1. Delivers a broader search result. Any search of this job title or any of its synonyms would result with this job showing up. 2. Send out more relevant and targeted match alerts to our own candidate database on to email alert vendors like zipreruiter 3. Get better performance from our distribution network by including our taxonomy job title as a keyword in the position description body 4. We can Predict the job performance and bidding in the correct CPC level out of the gate for this job type 5. Change the CPC level and perform numerus activities based on the prediction. So we campaigned this job based on the prediction provide by the system
Process Operator Job Performance Over Time
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Performance Graph
Apply clicks Applicants Views
What an efficient campaign prediction graph should look like
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Presentation Notes
Chemical operator job Ran quickly & then finished budget. This is what a good campaign should look like. What was the prediction for this job. No more spend after day 3 $15 budget This campaign quickly met the point of diminishing returns. So no sense spending more $$
Quickly Determine Out of the Gate Effective CPC
Process Operator Job Campaign
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AVG CPC
Date Dynamic Static Day of Week
25-Apr 0.25 0.2668 2
26-Apr 0.2475 0.2668 3
27-Apr 0.24 0.2668 4
Presenter
Presentation Notes
Lets look at how the bidding occurred on this job. The progress of the job performance was very strong out of the gate. This allowed us to drop the CPC level over time. 25 cents for this job type is a high bid out of the gate, but we did that based on the prediction. The CPC is changing in real time as results come in. The effectiveness can be seen in comparison with the static campaign which was not able to use dynamic bidding, even though we had a prediction.
Process Operator Job Visibility
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Presenter
Presentation Notes
Visibility on the results not on prediction mechanics And the ability to grab the wheel. Appended job title synonyms as key words to job description in campaign
Driving Quality Candidates
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#3 Blind SpotOptimizing the Campaign for Quality Candidates
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Quality Related Issues:
Over two-thirds of the TATech Job Board survey respondents reported that quality of job applicants is the single most important criterion for employers
75% of all candidate applies get screened out by ATS systems based on key word search (Bersin research study)
ATS quality metrics such as “Interview selection”, “hire”, etc., are latent data, not useful in real time bidding campaigns.
We need real time quality benchmark data on a per job basis
Presenter
Presentation Notes
How do you target the right audience in advance to reduce trial and error of acquisition spend? Targeting Human Talent is more complex than product advertising so it requires a more sophisticated targeting approach and qualification system. (Need more than just location and job title qualification in order to qualify and deliver quality candidates) Consumer markets call this audience targeting How do we predict Quality talent Much of the power of Facebook and Instagram’s advertising capabilities comes from unmatched targeting capabilities. Data-driven marketers know that experimenting with these targeting options is essential for scaling success. Whether you’re testing out Facebook’s Multicultural Affinity Targeting, reaching millennials with tailored ad creative, or leveraging Nested Lookalikes, boosting the relevance of your ads is a surefire way to convert more customers and drive higher revenue for your business. maximum relevancy Programmatic Creative, in which dynamic brand messaging and design was automated to serve an even more relevant message to the consumer. Programmatic personalization which has today become the top priority for agencies and brands. What are the next steps needed to complete the revolution of programmatic ad buying for recruitment? Can programmatic creative deliver a similar value in the recruitment space?
The “Process Operator” Job Example What Job Data Can Be Used for Qualification?
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Process Operator at La Porte, TX
Total is offering employment opportunitiesfor Operations Personnel at its La PortePolypropylene Plant in La Porte,TX and Bayport Polyethylene Plant inPasadena, TX.
Candidates will be required to successfullycomplete and pass extensive testing,drug/alcohol screen, background check,physical requirement evaluation and thecompany's Operator Training Program andprobationary period. Shift work andovertime will be required.
Minimum Requirements60 College Credit Hours (preferably inProcess Operations Technology) as of May31, 2016.Or Five years recent PetrochemicalIndustry experience (preferably in
operations)Or Four years of military experience
Job Title: Chemical Operator
Skills: Military Experience
Previous Experience: Operations
Education: Some College/University
Job Type: Shift work
Taxonomy Data Extraction
Qualified Applicant ResultsMatching Taxonomy to Candidate Application Enables RT Scoring of Each Applicant
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Presenter
Presentation Notes
Matching is part of the prediction as well as the campaign strategy. 80% is good match Bi report avg match score per vendor CPA report Fully Qualified: 3 Somewhat Qualified: 24 (most of them missing military experience or experience in operations in the Petrochemical industry ) Unqualified: 9
Qualified Applicant ResultsDrill Down to View Qualified Resumes with Matching Criteria
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Presenter
Presentation Notes
Matching is part of the prediction as well as the campaign strategy. 80% is good match Bi report avg match score per vendor CPA report Fully Qualified: 3 Somewhat Qualified: 24 (most of them missing military experience or experience in operations in the Petrochemical industry ) Unqualified: 9
Real Time Campaign Response:Adjust Campaign Based on Applicant Quality Scoring
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Low quality vendors cut off based on quality metric and budget reallocated to high quality producing sources
Employers have a quality indicator at the sourcing level separate from and more efficient than the ATS keyword based system
Presenter
Presentation Notes
Real time data on average candidate Match score per source vendor enables spending to be directed to the best source for each job type while campaign is running They also don’t have to be dependent on the ATS system for scoring or worse trying to get data out of their ATS system to pass back to the a campaign manager tool to use for Latent vendor qualification.
Programmatic Candidate Qualification Extends the Value Chain
Poll # 4 We know candidate quality is one of the biggest challenges in hiring today and measuring quality by applicant source is equally difficult. So, in the world of programmatic…
Predictive Analytics Drives Job Level Campaigning SuccessTargets and Engages the Right Talent Across the Web
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NLPParsing
MatchTitle
Job AdOptimization
Title& Skills Indexing
Targeted Distribution
Plan
Dynamic CPCBidding
QualityMonitoringBy Source
Budget Optimization
DATA: MATCH DATA, HISTORICAL PERFORMANCE, PREDICTIVE ANALYTICS
METRICS: JOB AD VIEWS, APPLICANT RESPONSE, MATCH SCORE
Sales Guru
The Impact of Predictive Analytics
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Presenter
Presentation Notes
Predictive analytics is to programmatic advertising what GPS is to self-driving cars… Without it, you will go somewhere, but the journey will not be optimized and it may not end well Now that we have a programmatic system with predictive analytics, the recruiter doesn't have to have their hands on the wheel and they can spend time doing more productive and if they want to sit back and enjoy the view.
