by Tim Williams
There have been many interesting articles recently about the ways in which influencer marketing is being transformed by social media, such as this piece from Sean Clarke, and this article from Intelegia, which give a great background into why and how brands are adapting their communications strategies to new media channels. An issue which receives less attention however is how companies subsequently measure the results of influencer engagement. This is a major challenge for many brands, but absolutely critical to success.
MEASURE AGAINST CORPORATE OBJECTIVES
When it comes to influencer engagement, many organisations continue to rely upon a gut feel that their efforts have worked, but have no way in which to measure the real impact of these efforts. Measurement is important for helping businesses understand not only how this form of influencer engagement compares with other marketing methods, but exactly what it is they are doing to drive the effect.
BIG DATA, BIG OPPORTUNITY
Change begets change, and as new forms of interconnectivity reshape the marketing communications landscape, new measurement techniques such as large scale data analytics are playing an increasingly essential role in influencer engagement programs. Big Data can be used to uncover influencers in relevant fields and measure engagement results in real-time, allowing brands to proactively re-align their influencer marketing campaigns, and feed this information back into their engagement strategies.
BEYOND SOCIAL MEDIA
Accurate identification and profiling of influencers is a key element of an effective influencer program. Someone who is influential in the area of music might be less so when it comes to consumer electronics or Hungarian politics, regardless of the size of their Twitter following or overall influence score! It is important therefore for businesses to differentiate between influence and popularity and engage primarily with those who are influential both within the framework of a brand’s defined business areas and within a wider context than a particular social media channel. While social networks represent an excellent opportunity for brands to communicate with end users they are not often a reliable tool for measuring influence.
PLAYING THE WEIGHTING GAME
The credibility of influence-measurement services such as PeerIndex, Kred and Klout remains a contentious topic (nicely summed up here). When it comes to measuring influence, it is important to remember that the primary objective of an influencer program is not just to influence the influencer. The real objective is to influence the influencer’s network, as this is the point at which the impact of influencer engagement is both felt and measured. Knowing how much ‘weight’ various influencers have within different topics and networks not only allows organisations to identify the best influencers to engage with, but also those who punch above their weight in terms of influence – these represent the ‘low-hanging fruit’ when it comes to engagement.
BUILDING YOUR INFLUENCER ENGAGEMENT PROGRAM
For a more in-depth look at influencer marketing, including a step-by-step guide to creating your own influencer engagement program, please check out our latest white paper.
Last week I gave a presentation with Samsung’s Oliver Harcourt at a market intelligence conference in Amsterdam.
The presentation is a case study of Samsung’s journey implement a more real-time and modern way of satisfying their market research and market intelligence needs.
The solution describe is based on Onalytica InfluenceMonitor and services the European HQ as well as a number of European countries.
The presentation details the initial business challenges, the proposed solution, the implementation challenges and the project’s success. Furthermore it looks at projects now being implemented to bring real-time insight and understanding to new groups within Samsung.
Drop me or my colleague Tim Williams (first.lastname at Onalytica.com) a mail if you would like to receive the presentation.
In a previous post I highlighted a small example of how the Onalytica Recession-Index gave a good indication of an impending recession in the UK.
However, I haven’t had the opportunity to conduct a more thorough analysis so I recently asked my colleague, Dr. Andreea Moldovan, to have a look at the Onalytica Recession-Index in relation to GDP. Her findings impressed me.
Figure 1 (below) shows the UK GDP against Recession-Index for UK Economy. The values for GDP are given in quarterly percentage (or relative) change on previous quarter.
The Recession-Index is a 1 month leading indicator and the values on the chart already contain the lead, i.e. Recession-Index at Q1 2010 contains actually data from Dec '09 to Feb '10, etc.
The series have different scales and are represented on different vertical axes, for ease of chart interpretation. The left vertical axis corresponds to the Recession-Index values, while the right axis is for the GDP.
Since Q1 2010 and except for Q4 2010, the Recession-Index correctly predicts the UK GDP direction of growth (increase or decrease) 1 month ahead. On the chart this is reflected by the series having opposite directions: decrease in Recession perception by the population corresponds to a growth in GDP and vice versa. So, in 8 out of 9 situations analysed the prediction is correct.
The lead of the Recession-Index is in practice more than 1 month as the GDP values are announced some time after the end of the quarter.
The same analysis for the US Economy and the conclusion is the same, that the expectation (or fear) of a recession by the population can be used to predict 1 month ahead the direction of growth rate (increase or decrease) of the US GDP.
Figure 2 (below) shows the quarterly values of US GDP (2011 revision) against the Recession-Index in the context of US Economy. As before, the Recession-Index already contains the 1 month lead on the chart, i.e. the Q3 2008 value actually refers to Recession-Index data for Jun '08 - Aug '08, etc.
This time the period analysed is longer, from Q3 2008 to Q2 2012. Except for Q4 2010 and Q3 2011, the Recession-Index is a 1 month leading indicator for the direction of US GDP growth rate (increase or decrease).
In summary, the Onalytica Recession-Index for UK and US respectively predicts the direction of the country’s GDP one month out in 87%(US) to 89%(UK) of the analysed quarters. I would not be surprised if a model, which takes a few more signals (maybe another Onalytica Index) could make the correct prediction every time. Stay tuned!
