When it comes to analytics, I am NOT a mathematician. But, I love persuasion through marketing. Other events I have presented: Indy Society Human Resource Management (SHRM) Consultants Forum MediaPost’s Email Insider Summit Broadcast Education Association Convention (BEA) Usability Professionals’ Association’s World Usability Day National Association of Broadcasters Convention, NAB
No magic equation for measuring online engagement. Why is this? Because engagement is just a buzz word. There is no one single specific piece of information we are talking about when we say “engagement.” Each company, product, service, and goal is different. Therefore, each must be measured differently. For each goal, engagement is different, and varies in degree for each goal.
Unique Visits : Shows how many people decided to engage with you for the first time by visiting your website. Frequency of Visit : Frequency must be contextualised within a specific time frame. A customer who has engaged 10 times with the company in the past 10 years has a lower degree of engagement for example in relation to a customer who has also engaged 10 times in the company in the last 2 months. Contextualised ‘frequency’ can therefore help us to identify the relative degree of our customers’ engagement. Recency of Visit : This metric speaks of the recency of our customers’ last engagement.
Although a deep journey signifies a high degree of engagement this metric again does not distinguish between the kind of engagement. Do your visitors passionately disagree with what you are writing about? Are they simply unable to find what they are looking for? In both of these cases a high degree of engagement may be of a negative kind. Like frequency, it therefore frames our customers’ degree of engagement only relatively. Depth of Visit : This tells us how many pages long our visitors’ journeys through the site were.
Time spent correlates with degree of engagement but as it does not discriminate between kind it may simply be negatively spent desperately trying to find the content your visitor is after. Similarly, most online metrics are only able to capture degree not kind of engagement: DO NOT HAVE TO READ THIS… Subscribing (feed, email, newsletter) Registering Feedback (comments, complaints, inquiries etc) Rating aggingfilteringookmarking its content User submissions (UGC) Printing or downloading a piece of content Brand index
“ If you are Georgia Pacific and you want people to be engaged in the Quilted Northern brand, its both the percentage of items/tweets etc., that recommend the brand — as in “wiping your butt with Quilted Northern is like wiping your butt with a cloud”– and the number of times consumers defend it — as in “I don’t care if its made out of the foreskin of an endangered species, I want the best for my baby.”
What comes out of the above discussion is that …it is impossible to derive the kind (positive/negative) of your visitor’s engagement using web analytics alone, and, therefore, that… …when we are talking about customer engagement in the context of web analytics, we are in fact talking about degree of engagement. This is not to say that we cannot make inferences and state hypotheses about the kind/content of engagement, based on what we can measure ( degree of engagement), nor that these hypotheses are unlikely to be correct. It is only to say that using web analytics it is impossible to make or support such inferences. Such inferences, about the kind of engagement, must necessarily be informed by considerations that lie entirely outside the field of web analytics. Before we begin making inferences on the basis of degree of engagement however let’s discuss this metric a bit more.
When it comes to DEGREE, we can make inferences Example : a frequent and recent visitor is ‘more engaged than’ someone who is not, but is he engaged? If yes how engaged is he? There is little we can do with relative statements such as this. In order to make such statements, we need to contextualize the component metrics that constitute a customer’s degree of engagement on a high/low continuum, beginning with apathy and proceeding with progressively higher degrees of engagement.
Each business is unique and each website is trying to accomplish something unique. Think of all the reasons a website exists, now imagine what engagement could be for each. Result : It is really hard to generalize, and That translates into a poor understanding of what is being measured. It is nearly impossible to define engagement in a standard way that can be applied across the board. Definitions that exist are either too broad (to cover every nuance) or too narrow (hence very unique). Result : Few people understand what you mean when you say “engagement”, and even fewer can then translate it to apply to their sites. Unlike clicks, visits, conversions, recency, ip addresses etc when you tell your management “engagement” it is hard to know what it is/means. At the heart of it, engagement tries to measure something deeply qualitative . Yet most efforts to measure it in our world tend to be hard core quantitative (translate that as: “ we have clickstream, let’s take our interpretations of what could possibly be happening, now find clicks that can carry the burden of our personal impositions, voila! here’s engagement “). Result : That mismatch is ok for a couple months, but as you measure it over time you’ll discover that it does not indicate true customer intent and will have sub optimal impact. A metric should be instantly useful . This one is not. Say you measure engagement. It could be a % or a absolute number or a ratio or whatever (in fact it can be any or all of those at the same time). You fire off a graph or a excel spreadsheet with trends. You repeatedly get asked: What are we measuring? Result : Little action. We should always try to have metrics that are instantly useful, you look at ‘em and you know what it is and if it is going good or bad. Most of all engagement is a proxy for measuring an outcome from a website. Conversion is not enough, as mentioned above, so we try something else. The problem that we’ll define engagement as a measure of some kind of outcome but we won’t give it the sexy name of engagement. Result : Confusion and delay occurs. For example, If we are measuring page views divided by unique visitors as a proxy of engagement (more pages per visitor means more “engagement”) they why not call that metric page view per visitor? At least that will make it clear what you are measuring.
With so many possible factors to look at, how do you know if you had successful engagement? That can mean profits. You sold stuff = Successful engagement. That can mean ideas. You got feedback on your product or service you can use = Successful engagement. That can mean referrals and recommendations. You got customers to tell other people you’re cool = Successful engagement. That can mean digital merit badges. You got people to link to you, follow you, Re-Tweet you = Successful engagement.
Each silo measures very different components of marketing effectiveness in very different ways. Some are shorter term and some longer term. Linking them algorithmically forces you to make some very large assumptions that may be unreliable in the face of actual marketplace dynamics. The net effect of all this uncoordinated measurement is that marketing gets lost trying to divine the true story of effectiveness of resource allocation from 100 data points on a three-dimensional scatter plot with no clear picture emerging. And while it may have been accepted practice in the past to throw this measurement the wall when asked about the payback on spend, today’s CEOs and CFOs have little patience for the fog of complexity.
structures many sources of information into a comprehensive, organized manner and presents the insights from each in a graphically related view facilitates the human brain’s power to find subtle, contextual links
This means that both the lowest (apathy) and the highest degree of engagement need to be defined. The easiest way to do this is to define the average degree of engagement (the average score for several metrics of your choice across your site or based on a competitor-specific or industry-wide benchmark ), considering everything that falls short of it as (increasing degrees of) apathy and everything beyond it as (increasing degrees of) engagement. In this way a customer’s degree of engagement assumes a non-relative meaning (it remains of course relative to your website’s, competitor’s or industry’s historical performance). DO NOT READ By inserting relative statements such as ‘x is more engaged than y if and only if x does z and y does >z’ into a continuum that is based on website competitor industry benchmarks, it is possible to provide a reference point which although relative in itself (historical performance) is sufficiently stable and pertinent to business performance, to provide with useful insights into visitor behaviour and businesscampaign success. (Substitute z with any or an aggregate of the visit metrics score(s)).
Use “Is it actionable?” to determine if you should measure it Time of user on site Geographic location who you are targeting
William Thomson, 1st Baron Kelvin (or Lord Kelvin ), OM , GCVO , PC , PRS , PRSE , (26 June 1824 – 17 December 1907) was a Scottish mathematical physicist and engineer . did much to unify the emerging discipline of physics in its modern form. his work on the transatlantic telegraph project he was Knighted by Queen Victoria , becoming Sir William Thomson. He had extensive maritime interests and was most noted for his work on the mariner's compass , which had previously been limited in reliability. Lord Kelvin is widely known for developing the basis of Absolute Zero , and for this reason a unit of temperature measure is named after him.
Utilize component metrics that constitute the degree of consumer engagement
http://www.longbowdirectmarketing.com/resources/help-center/Marketing-Effectiveness-Score Marketing Effectiveness Score What's in the MES In response to requests from our clients for a high level measure of their marketing programs, we are now calculating a Marketing Effectiveness Score (MES) for companies whose data we analyze. The MES blends six component metrics into an overall score that ranges from zero to one hundred. The components are: Churn—a measure of whether your company is gaining customers faster or slower than you are losing them Existing customer activity--measures how much of your customer population is buying at any one time Up-sell efficiency--measures whether you are improving the revenue you are getting from each existing customer Revenue growth— the bottom line question: Is your marketing working? Revenue at risk-- the ratio of the probability-adjusted revenue at risk to the total revenue; a measure of the exposure of the company to customer defection Cross-sell efficiency—measures how much of the spectrum of your product offerings is purchased by customers Loyalty profile—measures how close your customers comes to a revenue-optimized spectrum, and highlights any dangerous skews How the MES is calculated Calculates a raw score, often a ratio or percent, for each component. Then each raw score is transformed to a common scale, from zero to one hundred. Finally, the components are combined into an overall score. A bar chart is created, with a bar for each component, so companies can see which of the components pulled up their overall score and which ones may be pulling it down. We recalculate these numbers whenever we analyze a company’s transaction data, so progress can be easily tracked.
http://www.longbowdirectmarketing.com/resources/help-center/Marketing-Effectiveness-Score How to use the MES Once you look at the scores for the MES components, you can see which areas need work. Here are some component-specific tactics that can be applied to whichever areas need attention. Churn—If churn is high, typically the problem is the rate of defection rather than the rate of acquisition. You should deploy customer retention and win-back campaigns Existing customer activity—If new customer revenue swamps existing customer revenue, consider switching some the marketing budget from acquisition to existing customer marketing, especially up-sell and cross-sell campaigns Up-sell efficiency--Besides switching budget to existing customer activities, running reminder campaigns using up-sell predictions will improve this metric Revenue growth—Overall growth requires attention to all aspects of marketing. However the quickest, easiest, and cheapest way to raise campaign response rates is multivariate testing of offers and messaging Revenue at risk—The first step is to identify potential defectors, and then launch customer retention programs to keep them active Cross-sell efficiency—Find good customers who are buying narrowly from the product set and use cross-sell scores to make them attractive offers Loyalty profile—Use the Loyalty Profile chart in Longbow to identify underperforming customers (those in Lrank regions where the curve is concave) and run targeted campaigns with personalized offers to increase their revenue contribution
How do you define and measure engagement for your brand? Prescribe that definition in your goals and objectives when planning what you want to achieve with your social media programs. If you don’t do it there, you’ll be spitting and shitting all the same drivel the echo chamber does … “we have 10,000 Facebook Fans … we had 120 comments on the last blog post …” Good for you. But do those 10,000 Facebook fans do anything or seldom even look at your brand page like most “fans?” Do the 120 comments give you valuable insights on a potential new product feature or did four people get caught up in a flame war over who has the best collection of your refrigerator magnets? That’s what matters. “ Do you need to keep stats like a coach’s kid on all that other stuff? Sure. Someone will ask at some point. But the stat sheet only gives you a glimpse of the team. The fans see the all-star point guard scores 25 points per game. The coach sees he favors his right side and is confused by the match-up zone. Are you looking at the game or the stat sheet?”
http://www.searchenginejournal.com/the-ppc-roi-calculator-how-to-forecast-and-optimize-your-ppc-roi/10835/ f you haven’t started PPC yet, you can forecast your ROI. If you’re already running PPC, you can play with the metrics and see how you might increase ROI. You can increase ROI by improving its component metrics. The components of PPC ROI are: Cost Per Click (CPC) Conversion Rate (CR) Average Sale Want to change your ROI? Return on investment increases when more money comes in, decreases when money goes out. So… If average sale increases, so does ROI. If it decreases, ROI decreases. We want average sale to increase. If conversion rate increases, it takes less ad spend to get a sale, so ROI goes up. We want conversion rate to increase. If cost per click increases, ad spend does too, so ROI decreases. We want cost per click to decrease. How to Forecast Pay Per Click ROI In order to do this, you need a few pieces of information… or you can guess at them. Monthly ad spend: your client may provide this, or you can recommend one. We prefer clients with a minimum of $5k monthly spend. But some go into the hundreds of thousands of dollars. Conversion Rate: your PPC CR will differ from the rest of your site, depending on how well you optimize your account structure and landing pages, but you can start with one that’s based on natural search traffic. If your site is new and has no traffic, PPC will help you find this value. Hopefully, your ecommerce site conversion rate is 1.00% or above, and your lead generation site gets 3.00-5.00% or more. Yeah, the FutureNow guys expect better- good for them and their conversion optimization friends. If you aren’t there yet, use my values. If your CR is lower than what I suggest, your site needs work. Cost Per Click: get this from AdWords’ keyword tool and traffic estimator, or third party services like KeywordSpy and SpyFu . You’ll want a CPC that’s an average for the niche you’re targeting- you can look at the CPC for the most popular terms, but your average is going to be somewhere below that, if you use mid and long-tail keywords. Average Sale: if you don’t have this from your website, base it on offline sales, or a realistic purchase. (Are customers likely to buy just one? What’s the price? Use that.) Enter these into the PPC ROI Calculator and hit “calculate”, and stare in amazement at the results. You’re welcome. How to Optimize PPC ROI With The PPC ROI Calculator Once you’ve taken a look at your real metrics, you can play with the values to see how you might realistically increase ROI. Here’s how you could tactically optimize the components of ROI to achieve that goal. Cost per click (CPC) Can you get any benchmarks or look at other businesses doing ppc in the same vertical to find out if your CPC is higher than average? Or, again, try KeywordSpy and SpyFu . Have you optimized your quality score, or is it below 7 for a lot of your keywords? Are you spending a large percentage of your money on keywords with low quality score? Improve your account structure, write and test ads to get a higher CTR, make sure your landing pages fit with the keywords and ads that precede them in the user experience. Make sure you read my post about CTR and quality score . Are you allocating as much of your budget as possible to the keywords that are both low CPC and high ROI? If not, move them into their own campaigns so that you can. Read more about PPC budget optimization here. Head back to the PPC ROI Calculator and see what decreasing CPC 10% or 20% would do for your ROI. Average Sale (Value/Conversion) Sometimes specials and discounts govern the ultimate selling price. Collaborate with whoever creates these offers so that you can test offers and ads together for optimal ROI. Strike a balance between lowering price and increasing CR. If you often sell multiple products per conversion, can you incentivize to increase cart size? Upsell and cross-sell your heart out. Use the PPC ROI Calculator to see what increasing your average sale by 10% or 20% would do to your ROI. Conversion Rate (CR) For ecommerce, I like to see at least a 1% CR. For lead gen, at least 3-5%. I consider those acceptable minimums. If you’re below this, you need some design and usability improvements. Look at the leaders in your vertical and see what they’re doing differently. In particular, look at the sites that consistently place PPC ads in the top 4 positions in your vertical. If they’ve been doing it for a long time, they’re probably getting enough ROI to justify continuing, and that means they’re probably getting a decent conversion rate. Go back to the PPC ROI Calculator and see what an acceptable minimum CR would do to your ROI. Or if you’re already there, raise your CR by 20% and look at what that does for your ROI. Work with web designers to improve conversion rate. Integrate conversion optimization into redesign strategy. After trying out improvements in ROI component metrics in the PPC ROI Calculator and thinking about what you realistically can improve in your PPC accounts and websites, take action. Improve those metrics!
The New Online Analytics Dashboard
The New Online Analytics Dashboard
Online Engagement Marketing January 27-29, 2010 Washington, D.C. prebynski.com twitter.com/prebynski facebook.com/prebynski Brandon Prebynski
We will “discuss the new
online analytics dashboard.” <ul><ul><li>What does it mean to measure online engagement? </li></ul></ul><ul><ul><li>Your Online Dashboard </li></ul></ul><ul><ul><li>Component Metrics </li></ul></ul>
Managing Director of Education for
the Web Analytics Association Jim Novo “ A customer whose last engagement with a brand is more recent than that of another is also likely to be more engaged.” This metric again does not distinguish among the kinds of engagement. Depth of Visit
Time Spent on Site This
metric again does not distinguish among the kinds of engagement. Marketing Executive at cScape in London Theo Papadakis
“ Linking, bookmarking, blogging, referring,
clicking, friending, connecting, subscribing, submitting inquiry forms and buying are all engagement measures at various points in the customer relationship.” Lee Odden CEO of Top Rank Online Marketing
“ As always, it depends
on the audience and the goals. If the audience is employees and you want employees to be engaged — we measure it by the percentage of employees that contribute to Yammer, the degree to which people read and comment on our internal blog, the increase in internal referrals, reduced turnover rate, etc.” Katie Paine CEO, KDPaine & Partners
(conclusions) Analytics Evangelist for Google
Avinash Kaushik No web metric, or combination of metrics, can discriminate between kind of engagement i.e. positive vs. negative. All web metrics can do is discriminate between relative degrees of engagement. A customer with a high score in his visit metrics may nevertheless feel apathetic towards the brand.
“ Engagement is not a
metric that anyone understands, and even when used, it rarely drives the action / improvement on the website.” Analytics Evangelist for Google Avinash Kaushik “ Because it is not really a metric, it is an excuse.” “ Why?”
<ul><li>Each business is unique and
each website is trying to accomplish something unique. </li></ul><ul><li>It is nearly impossible to define engagement in a standard way that can be applied across the board. </li></ul><ul><li>At the heart of it, engagement tries to measure something deeply qualitative . </li></ul><ul><li>A metric should be instantly useful . This one is not. </li></ul><ul><li>Most of all engagement is a proxy for measuring an outcome from a website. </li></ul>Analytics Evangelist for Google Avinash Kaushik
“ Whether or not an
audience is engaged depends on who they are, who you are in relation to them, what medium or tool the communication is occurring in or on, and a whole bunch of other factors.” Consultant, SocialMediaExplorer Jason Falls
“ Marketing departments measure payback
in a disjointed series of technically sound but ad-hoc ways in four distinct measurement silos.” Pat LaPointe Managing Partner at MarketingNPV customer metrics unit metrics cashflow metrics brand metrics
With these silos, how do
we clarify the predictive drivers of the business? Pat LaPointe Managing Partner at MarketingNPV not possible to do this scientifically since it’s not an econometric modeling problem solvable by equations and computers
Bonus: PPC CALCULATOR: The components
of PPC ROI: Cost Per Click (CPC) Conversion Rate (CR) Average Sale http://www.searchenginejournal.com/the-ppc-roi-calculator-how-to-forecast-and-optimize-your-ppc-roi/10835/