Twitter Sales Reps add crucial value to Twitter Targeting

You’re almost closing an important sales contract. Then, your prospect client, eager to drive his business, asks this tricky question: “…but how do you exactly reach my specific target group?” Being a Twitter sales rep you know that standard targeting might fall short.

In those cases, MySocialDatabase can provide you with hyper-targeted audiences to reach the exact right audience for your client. 

Premium Audience

MySocialDatabase (MSD) is the exclusive Twitter audience partner for renowned ad agencies, such as BrandDeli. MSD completes the digital chain by offering the solution to reach relevant audiences across all industries. Based on an enriched database with 330 million Twitter accounts we select premium audiences to drive the best campaign results.

Twitter sales representatives work together with MSD as follows:

1.      Twitter sales team receives a campaign briefing from the client.

2.      Discuss possibilities with MySocialDatabase:

  • Audience sample (can be used to convince client)
  • Fixed fee for the audience (approx. 1000 euros per audience)
  • Determine audience size (To ensure that you can spend the total budget )

3.      Twitter Sales Team decides whether or not to proceed.

The impact of enriched Twitter data

Hyper-targeting is making the right connections and address them with the right content. By focusing on relevant connections you are able to expand your network. The digital marketing principles are simple: content is king. But the king is nothing without having good team members – read: data - around him on the playing field. The combination of strategy and data is the way to result.

One of our nicest examples of putting data and content together is the Vauxhall case.  In close cooperation with the London Twitter sales team, the challenge was to reach the true football fans in the UK. We detected 85 listings based on first names for sending personalized video messages.

According to Andrew Curley, head of sponsorship: “This is a UK first for Twitter to give true football fans a unique surprise. So far the feedback has been extremely positive and the amount of engagement we have seen has been fantastic.”


How do we do it?

The process

When we create a new audience, we follow a procedure to ensure that our clients are targeting the right audience. Please find below the list of steps that we follow when we create a new audience. Please keep in mind that the procedure may vary by audience.

  1. Social Database >> 700 million users
  2. Bio search
  3. Interests / passions / beliefs
  4. Statistically relevant accounts
  5. Gender & location algorithm
  6. Find Most Important People
  7. Word analysis
  8. Download current following
  9. Relevancy algorithm
  10. Find relevant influencers
  11. Download all followers
  12. Exclude irrelevant accounts
  13. Exclude other countries
  14. The community
  15. Apply filters and exclude waste

Example audience: Medical Oncologists.

We start with our Social Database, where 700 millon Social Media users are segmented and updated using multiple APIs. In this example, we start off with searching for the word ‘’Medical Oncologist’’ in Twitter bios. This search is based on self-reported data. Next, we download all accounts followed by these users. Then we remove statistically-irrelevant accounts and use our gender algorithm to define male/female/company/group accounts. After that, we look for the most relevant people based on our data, in this particular case ‘Medical Oncologists’. We also look for the most common words used by these oncologists in different languages. Then we download the most important accounts currently followed by the oncologists and we use our relevancy algorithm to define that. Once we have found the oncologist influencers, we download all followers of these influencers who are following more than for example 10 of these accounts. This number depends on the audience. In this case, we look at the most relevant number of accounts that is followed by real Medical Oncologists. After that, we exclude irrelevant accounts such as people with an inconsistent following behaviour and people from outside your target region. By now we have not only found the accounts based on self-reported data but also people who have the same following behaviour as an oncologist. The large majority of this audience will consist of real Medical Oncologists. However, in some cases the audience include other people who are not a Medical Oncologist even though they behave like one. In most cases, these people will be other Medical Professionals who work closely with Medical Oncologists. It is beneficial to include them in this audience as they will help us to get the message across. By targeting such a community however, we have a small risk of targeting patients in the unlikely event that a patient is behaving like a Medical Oncologist. Therefore, we exclude a list of words frequently used by patients such as patient, survivor, fighter and many more. Finally, we have created a global audience of patients based on keywords and behaviour. We exclude this audience in every campaign.

Hashed audiences

For privacy reasons, we will never share completed audiences in a way that user data is readable. Therefore we hash the data and we upload the hashed file into the Twitter Advertising accounts of our clients.

About SHA-2

SHA-2 (Secure Hash Algorithm 2) is a set of cryptographic hash functions designed by the United States National Security Agency. Cryptographic hash functions are mathematical operations run on digital data; by comparing the computed "hash" (the output from execution of the algorithm) to a known and expected hash value, a person can determine the data's integrity. For example, computing the hash of a downloaded file and comparing the result to a previously published hash result can show whether the download has been modified or tampered with. A key aspect of cryptographic hash functions is their collision resistance: nobody should be able to find two different input values that result in the same hash output.