The Google AutoComplete Dilemma Generally, whenever clients or prospective clients request assistance removing negative search engine results, it?s the simple yet complex matter of enhancing positive search results to overtake the negative. Nevertheless, therein lies the shadow often overlooked by most reputation management firms. Regardless of the amount of time and effort expended in the name of suppressing negative search results, Google uses AutoComplete to remind web searchers that these negative events did occur, even before they hit enter on their keyboard. Open a new tab and type a search term into Google. In the majority of cases, Google will display a dropdown lost of bolded search terms to choose from based on popular searches containing your starter term. Sometimes, these results might now be positively-conveyed. This is where Google AutoComplete manipulation is most useful. I have had clients with AutoComplete results ranging from mugshot to sex offender to arrest to mobster and beyond. Research into eradication methods for negative AutoComplete entries come up relatively dry, garnering a good bit of discussion with no solid solutions. There is a plethora of opinions as to how Google arrives at the AutoComplete results which show up, as well as theories on ways to remove entries from visibility, but there is little in terms of documented, proven cases of AutoComplete entry removal for specific search terms. So how can you as an online reputation manager for yourself or others remove these results? The idea is derived from the general online reputation management concept of negative search result suppression through positive result enhancement, which buries negative results to the point where they are unseen by the overwhelming majority of web searches. This method carries over into AutoComplete manipulation in that it is far easier to suppress negative AutoComplete entries than to attempt term removal. In layman?s terms, I am going to explain how to force out negative AutoComplete results by replacing them with positive. This is the ?one amazing trick that Google hates!?. There was a period of time long ago when Google was displaying up to ten AutoComplete results for any term that was searched. As time has progressed, Google has shortened the number of entries shown, and most search terms have anywhere between two and six terms considered ?related?. What does this mean for you, the online reputation manager? You need to have a minimum of six positive or neutral spun search terms prepared that you wish to be appended to the end of your brand (your business, name, etc) when searched in Google. Using Reputation Management Tactics The first step in the AutoComplete manipulation process is to fully ensure that the search engine giant has a high-quality reason to supply these appendages to your brand?s search results. Prior to diving into changing your initial AutoComplete result to ?hero?, build up some writing, articles, press releases, social media posts, and other content to support the term being attached to your brand. The main Slate ORM website has many (currently under construction) articles and guides available for free, as well as more in-depth guides for sale, to assist in detailing the more efficient options to make the content farming process smooth. The most frequently-considered factor for the Google AutoComplete process to decision which terms to append to the end of any given initial search is the number of independently-made searches for a specific term. It was stated by a Google spokesman that the AutoComplete function is nothing more than a reflection of the most commonly searched terms in the past with words entered, instead of the search engine itself using algorithms to generate suggestions. For this reason, in order to properly maintain a solid AutoComplete manipulation campaign, a reputation manager needs to find a method for obtaining a large number of individuals to search for specific terms. The method with the greatest success that I have used is hiring micro workers on Amazon?s Mechanical Turk to complete specific web searched of terms of my choosing, paying them a couple cents for their efforts. All in all, what you need to take away from this is the following formula: MTurk + $30 + Patience = AutoComplete Manipulation. What is Amazon?s Mechanical Turk? A fair enough question, as with how widely-known Amazon is, MTurk is rarely mentioned or spoken about. Amazon?s Mechanical Turk is a massive online marketplace that ?requestors" use for crowdsourcing their digital tasks to ?workers?. These tasks are generally too complex for computers, yet too widespread to demand from a small group of individuals. The concept works through the use of micro workers, who create accounts and scour listings of campaigns for ones that interest them. For example, taking a quick 30-second survey on the Mechanical Turk site itself could net a worker twenty cents, or transcribing a fifteen minute audio file could net them forty dollars. To use these workers to your advantage when creating a smooth-running machine for manipulating your Google AutoComplete entries, you will need an account as a requestor. For this, you will also need an attached and funded Amazon Payments account, as well as a general Amazon account. If you don?t have these set up, do so, and load $30 into your Amazon Payments account before moving onto the next step. Once you are done, the world opens up: How To Set Up A Project Click Create at the main Requester screen, then New Project, use the Data Collection category, and click Create Project. Name the project something memorable for you, noting that this name is not seen by workers. After this, come up with a descriptive title that is seen by workers. I generally choose something generic yet self-explanatory, such as ?Google a specific search term, click the second link, tell me a certain word in the most recent blog post?. Under keywords, I use data collection, search, and web search. The reward per assignment I set at a flat seven cents per completed HIT, with the total number of assignments per HIT set at 300. This means that, after 300 completed and submitted searches, the HIT will expire, ensuring that your $30 budget has been maintained. Seven cents for each of 300 assignments is $21, and Amazon Mechanical Turk scrapes another three cents per assignment off the top, leading you to your $30 total. I allow only two minutes for the process, and set the HIT to expire after 14 days, leaving the auto-approve at the typical eight hour limit. This means that once a Worker clicks on the assigned HIT, they have only two minutes to complete and submit. After eight hours from when they completed and submitted the assignment, they are paid. This happens either 300 times, or consistently for 14 days, whichever occurs first. Under Design Layout, I put ?Find the URL as a result of a Google Search? on the first line. The bulleted list goes as follows: Google search "[TERM] [Target AutoComplete]? (no quotes) Click the third result Tell me the URL of the referenced link And the URL field is left as-is. Preview the HIT, ensure that everything flows as it should, and finish. To Conclude Rinse and repeat the process for as many AutoComplete terms as you?d like. If you are attempting to suppress a negative AutoComplete search result, I?d suggest doing this seven times with different positive AutoComplete search terms you?d like to replace the negative term with. Stagger each new campaign weekly, and in nine weeks you should be all set. Typically results will show up in AutoComplete up to two weeks after the campaign finalizes, so relax and give Google a chance to recognize all of these new worldwide searches. From Slate ORM, adapted and expanded from a post by Lauren Starling.