As presented at the eMetrics Summit in Chicago on June 10, 2015, in this fast-paced session, Jimmy Wong from LinkedIn shares five ways in which marketers at the world’s biggest professional social networking company use custom-built analytical tools and techniques to acquire, delight, and retain B2B customers. See how a combination of open-source and commercial technologies can be leveraged to hack together optimal solutions for marketing. Learn how these in-house techniques can be applied to your own organization.
Jimmy Wong heads up the business analytics team for LinkedIn’s B2B marketing. At the intersection of marketing and technology, Jimmy has extensive experience in building scalable analytical products and optimizations for B2B marketing and sales. He also runs the local Toastmasters club and mentors college students on starting their careers in marketing and analytics. Jimmy is passionate about empowering marketers to succeed through analytics.
CI, CD -Tools to integrate without manual intervention
Five Ways Analytics Empowers Marketing LinkedIn eMetrics Jimmy Wong
1. Five Ways Analytics Empowers
Marketing at LinkedIn
Jimmy Wong
Senior Manager, Business Analytics
LinkedIn
eMetrics Summit, Chicago, June 10, 2015
https://www.linkedin.com/in/jimmywong
@JimmyWongUSA
2. Our mission is to connect the
world’s professionals to make them
more productive and successful.
2
Jeff Weiner
5. 5
How does LinkedIn make money?
Source: https://press.linkedin.com/about-linkedin as of May 25, 2015
6. Be the most effective
platform for marketers to
engage with professionals
6
LinkedIn Marketing Solutions’ Mission
7. 7
LinkedIn Marketing Solutions Connects
B2B Marketers with the Right Audience
Source: As calculated by comScore, Aug 2014 Mediametrix data for US
Members
worldwide
364M+
More business decision
makers than comparable
business sites
2.8X
Higher buying power
index vs comparable
business and social sites
28%
9. "In the recurring revenue world,
there's no such thing as
post-sales. Every single activity
is a pre-sales activity.”
9
Dan Steinman
Chief Customer Officer
Gainsight
14. 14
Optimize the Top of Funnel
Awareness
Consideration
Decision
ToFu
MoFu
BoFu
Engage with
sales
Visit website
Submit inquiry
Propensity
Modeling
for
Look-alike
customers
7x higher
inquiry rate
* From internal assessment of LMS marketing campaigns using the propensity modeling, April 2015
15. Example Propensity Model based on Skills
Rank the top skills of
customers to score
top skills
Find look-alikes with
the correct skillset
NEW PROSPECTS
CUSTOMERS
BY PRODUCT
Top Skills
Skillset A Skillset B
LLA
InMail
Text Ads
Sponsored
Updates
Display Ads
Products
Emergent Skillsets
High NPS or high spend customers
16. 16
We Leverage Open Source Technologies When Possible
Orange
Data Mining
Python
Teradata (not open
source) or other
SQL database
R
Hadoop
17. 17
Lessons Learned about Customer Reach
Clone your customer list with propensity
modeling
Let your data guide you on developing personas
Invest in data and analytics
Consider open source and in-house skills
Consider commercial analytics services
19. 19
Nurture Leads Until They’re Ready
Awareness
Consideration
Decision
ToFu
MoFu
BoFu
Engage with
sales
Visit website
Submit inquiry
Use Anonymous Nurturing
With LinkedIn Lead
Accelerator
Low score: Continue to nurture by email
High score: Send MQL to sales
21. 21
Lessons Learned about Customer Nurture
Send priority leads to sales with predictive lead
scoring, otherwise nurture other leads
Consider using a product like LinkedIn Lead
Accelerator to nurture anonymous visitors to
convert into leads
23. 23
Red Carpet VIP Onboarding at Scale
Surprise, delight, retain
Surprise and delight segment of
highest spending new ONLINE
self-service customers within 30
days of activation
Gift basket includes:
• Handwritten Welcome &
Thank You card
• LinkedIn Ads Playbook
• Assorted treats
100% outsourced fulfillment for
scale. Vendor under
confidentiality and privacy
agreement.
24. 24
Retention Rates and Spend Behavior
Improves with Red Carpet Treatment
0%
10%
20%
30%
40%
50%
60%
70%
80%
M1 to M2 Survival Rate M2 Spend Profile: Spend > $1,000 in M2
Survival Metrics: Control vs. Red Carpet
Control
Red Carpet
+ 7%
+ 87%
* From A/B split testing results of online advertiser onboarding, 2013
25. 25
Lessons Learned about Customer Retention
Delight your best customer segment with an
unexpected surprise welcome gift for retention
Find ways to build customer relationships both
online and offline for retention
Automate and outsource repeatable processes
for scale
Use the analytics team to define the best
customer segment and enable automation
27. 27
Upsell from Free to Premium
Inspire potential customers with Recommended Sponsored Updates
How to increase number of
Sponsored Updates customers?
Example Company Status Update
①Identify the best company
status update for each
potential customer
②Personalize email creative
③Automate for scale
Free Paid
28. 28
An Email Worth $1.6 Million per Year
* Estimated yearly
contribution from this
marketing program
29. 29
Lessons Learned about Customer Upsell
Consider providing a free service to increase the
user base for more potential upsell
Use data to personalize upsell message with:
Right person
Right timing
Right offer
Automate for scale
33. Machine Learning Accounts for 56% of Marketing Acquisitions
Monthly subscription marketing acquisition
Online sourcing business
Total Prediction
Power
Propensity
Model
56%
Signal Based
Marketing
34%
Profile Based
Marketing
10%10%
34%
56%
100%
33
34. Analytics Enables Personalized 1:1 Marketing
34
Steve
Score = 0.9
Emily
Score = 0.9
Behavioral Score = 0.5
Identity Score = 0.3
Social Score = 0.1
Behavioral Score = 0.2
Identity Score = 0.3
Social Score = 0.4
... With a premium
account, you get
more search results
and access to …
... Did you know 5
of your connections
have started to use
premium account
…
37. 37
Lessons Learned about Optimization
Human Learning + Machine Learning
= Maximum Impact
More Data = More Power
Analytics enables 1:1 Marketing
Automate for scalability
38. In God we trust,
all others must bring data.
38
W. Edwards Deming
Question: By a show of hands, how many of you currently use LinkedIn for sales and marketing purposes? Awesome, look around--these are the cool people in this room!
Good morning everyone! My name is Jimmy Wong and I will be sharing with us here today a glimpse into how LinkedIn the company executes on its marketing strategies powered by analytics. By the end of this session, I will hope to have given you a few more ideas that you can take back home and apply to your business.
First, a little background about myself. For the last 4 years, I’ve been working at LinkedIn Corporation in the San Francisco Bay Area. I’m currently heading up our business analytics team covering Global Marketing Operations as well as for the marketing department of our Marketing Solutions business unit, where we market to marketers to use the LinkedIn platform.
It’s great to be back here in Chicago today. I remember the first time I visited Chicago was about 20 years ago. My college UCLA sent me here to represent the college in the final round of the National Student Design Competition, where we needed to build a machine to carry a pile of pennies up a flight of stairs powered by only a single AA battery. Unfortunately I did not win, but I did get a free trip to Chicago and got a chance to go visit the famous Sears Tower, the world’s tallest building at the time. It’s great to be back in Chicago spending time with all of you here today.
At LinkedIn, our company’s mission is to connect the world’s professionals to make them more productive and successful.
Photo credit: http://tmblr.co/ZHTARn1cK8IZb
As the world’s biggest professional social network, LinkedIn has 364 Million members worldwide using the platform, and growing at a rate of more than 2 new members each second.
LinkedIn has a Freemium business model. Anyone can register a new LinkedIn account for FREE and be totally successful in using LinkedIn to build up their professional identity and network, for the purposes of either looking for their next career opportunity or consuming professional content and news each day to become even more successful in where they are right now.
Besides the value provided to individual members, LinkedIn also provides a platform for our Business Customers to Hire, Market, and Sell. Companies can reach out to job-seekers, to audiences, and to new customers through LinkedIn.
Keep watching how LinkedIn revolutionizes how business gets done.
Overall, how does LinkedIn make money? For an internet company with about 8,000 employees, LinkedIn’s business model is well diversified.
62% of the revenue comes from the Talent Solutions business unit which provides services to recruiters and job seekers
19% of the revenue comes from our Marketing Solutions business which addresses the needs of Marketers using LinkedIn
And another 19% of the revenue comes from our Premium Subscriptions business for sales professionals and others who want to upgrade their account on LinkedIn
As many of us here today are involved with marketing in one way or another, I will zoom in on how our LinkedIn marketers from the Marketing Solutions business are “marketing to marketers”, and the role that analytics play there.
First of all, the mission of the LinkedIn Marketing Solutions business is to “Be the most effective platform for marketers to engage with professionals”
Photo credit: http://tmblr.co/ZHTARn1cK8IZb
For marketers, the process starts with finding the right people, and LinkedIn is the best place for that.
We have 364M+ professionals worldwide. These professionals are highly educated, affluent and influential.
Our LinkedIn customers come from this audience. Furthermore, our customers’ customers from various industries also come from this audience on LinkedIn.
My job is to help marketers, both internally at LinkedIn, as well as externally among our clients, to utilize the power of analytics to engage with audiences.
Our LinkedIn customers are in different stages of relationships with us.
Different marketing activities are needed in different phases of the customer lifecycle.
We’ll explore these 5 customer lifecycle activities today, with example cases from LinkedIn marketing:
1. Reach new prospects
2. Nurture them until they are ready to purchase
3. Retain the customers
4. Upsell the customers to additional products and services
5. And Optimize the entire closed-loop process to transform into a marketing machine
Take a careful look. Is there anything missing here?
Note, I did not explicitly set aside a separate lifecycle stage for customer acquisition. As far as customer acquisition goes, acquisition should be considered a part of every single phase in the demand generation process, rather than as a separate standalone process activity.
Photo credit: http://tmblr.co/ZHTARn1fdhKMf
In fact, Dan Steinman, Chief Customer Officer at Gainsight, says:
"In the recurring revenue world, there's no such thing as post-sales. Every single activity is a pre-sales activity.”
Who would NOT want to have recurring revenue? For a demand generation marketer, acquisition isn’t part of your business goals, it’s all your business goals.
Marketing can play a big part driving recurring revenue over the entire customer lifecycle.
Photo credit: http://tmblr.co/ZHTARn1cK8IZb
In the immortal words of author Lewis Carroll in Alice in Wonderland, let’s “begin at the beginning” with
Reach, where we want to find new customer prospects.
Photo credit: http://tmblr.co/ZHTARn1fdhKMf
For understanding the purpose of reach, let’s quickly review the Demand Generation Funnel. We’ll follow the journey of a particular buyer—let’s call her Sally—who is a marketer at a medium-sized business services company interested in using LinkedIn to help her find more customers.
Let’s start at the Top of the Funnel, or shortened to ToFu here. Our marketing objective at the Top of the Funnel is to generate awareness of our company and the particular customer pain points that our company solves. We are successful when the suspect Sally’s interest is piqued enough to have her visit our Business.LinkedIn.com marketing website and blog.
Like many other marketers, the marketing tactics we use at LinkedIn at the Top of the Funnel include SEO and Paid Search, as well as online advertising and social media. Besides having a presence on Facebook and Twitter and AdWords and YouTube, at LinkedIn our marketers rely a lot on what we call LOL, or LinkedIn-on-LinkedIn. With LOL, we “sip our own champagne” where our internal LinkedIn marketers use our own LinkedIn products to reach out to engage with audiences. These LinkedIn products include Slideshare, LinkedIn Display Ads, Text Ads, Sponsored Updates, and Sponsored InMail. At LinkedIn, we also have the luxury of using email marketing for reach because of our big database of 364 Million members—we just need to properly segment the members for relevant messaging and timing, and that’s where analytics plays a big role. I’ll speak to that more in just a little bit.
Next step along the buyer’s journey is the Middle of the Funnel, where now the prospect Sally has visited our website and is considering our company’s specific products and services in comparison to other solutions. Our goal with the Middle of the Funnel is to have Sally build enough trust with us to have her submit an inquiry with her name and contact information in order to get something like a whitepaper download, a webinar, or a demo. The analytics-based marketing tactics we would typically apply for this middle of the funnel portion would be Landing Page Optimization. I won’t go into this area much today though.
At the very Bottom of the Funnel, after the prospect Sally has submitted an inquiry with us with her contact information and is now considered a lead, we start the Decision phase. We would apply some lead scoring rules to either route Sally to the sales team if she is a highly qualified buyer, or otherwise to a marketing nurture stream to keep her warm until she becomes better qualified. Ultimately, we want Sally to decide and commit to a purchase later down in the rest of the sales funnel.
As mentioned before, reaching out to specific suspect customers at the top of the funnel is possible because of our big 364 million member database at LinkedIn. As a members-first organization, we want to make sure that we target only the most relevant members at the most relevant time with the most relevant message—and avoid Spray and Pray marketing.
Our analytics team has created various marketing propensity models for identifying look-alike customers based on LinkedIn data. The propensity models permit very refined segmentation and targeting based on demographics, behavioral, transactional and social data beyond the basic job function/industry/geography targeting.
As a result, in a recent Marketing Solutions email campaign, we were able to generate a 7x increase in the prospect inquiry rate based on the use of propensity modeling, based solely on more accurate and relevant targeting.
With the look-alike modeling, how do we do that? How do we clone Sally and find more new customers just like her?
I’m going to walk you through one example of how our analytics team has built a propensity model for marketing. The goal of the new propensity model is to identify the top marketers on LinkedIn who would be most likely to be interested in learning about LinkedIn Marketing Solutions and eventually commit to a purchase.
We start off by identifying the best existing customers that we want to clone. It could be people like Sally who have already spent the most on LinkedIn marketing solutions products and who may also have a high NPS or Net Promoter Score, which is one type measure of customer satisfaction or brand loyalty. For some new products without much of a customer base yet, we can also use our list of blog subscribers who have opted in to receive notifications of new blog articles as the set of people we want to clone.
In this example, the customer’s list of skills is used to identify other potential customers with similar skills. We combine with other known data about these people, such as their job seniority, function, and industry, as well as other behavioral, transactional, and social data to build a propensity model that predicts each member’s likelihood to purchase.
The propensity model can be used to distinguish members with a better fit for different products as well. Our product marketing team is starting to use the learnings from the propensity models to refine data-driven personas.
The end result of incorporating the propensity model in the marketing campaign would be an increase to the email Click-Through-Rates and subsequently Inquiry rates and Closed Won rates.
Although direct response marketing has been around for a long time in various industries, it’s the recent availability of Big Data that has made such data-driven marketing possible in even more industries and accessible to even more practitioners.
The cost of collecting and storing information has decreased dramatically over time. Open source technologies have also played a big part in the recent data revolution.
On my analytics team, we prefer to use the open source Python computer language for processing the predictive models, including the Orange Data Mining library, and supplemented by work within R. We also use Hadoop and relational databases such as Teradata for data retrieval and data prep. As a rule of thumb, the data prep and cleansing effort would usually take around 85% of the time in a predictive modeling project, and the actual predictive modeling itself would take only 15% of the time.
Other than Teradata on this list, you can download all of these on your own for free and start using them right away.
However, downloading open source software doesn’t produce results without the right people using the software. For the make-vs-buy decision for advanced analytics, your specific company and industry may have different criteria than an internet company like LinkedIn. For LinkedIn, we’ve definitely made the investment in staffing and skills for managing data and information in order to stay competitive.
Lessons Learned about Customer Reach
Clone your customer list with propensity modeling
Let your data guide you on developing personas
Invest in data and analytics
Consider open source and in-house skills
Consider commercial analytics services
Photo credit: http://tmblr.co/ZHTARn1cK8IZb
We’ve looked at extending Reach. Next, we’ll look at customer nurturing activities
Photo credit: http://tmblr.co/ZHTARn1fdhKMf
After the prospect Sally submits her contact information on our marketing website, we tag her inquiry with a predictive lead score, based in part with the propensity scores mentioned earlier. If Sally has a low lead score, then her contact info would be put into a marketing nurture email campaign until time her lead score becomes high enough to be prioritized for our busy sales development team. If Sally has a high lead score, then she would be considered an MQL, Marketing Qualified Lead, and her information would be provided to the sales development team to engage.
What if Sally had originally visited our marketing website, but was not ready yet to submit her contact information. Perhaps she was browsing through some of the ungated content on our website like fact sheets and customer case studies, but she either was interrupted or wasn’t ready to talk to someone at our company yet. It’s like she came to the front porch of our house, looked around on the outside, then left without knocking on the front door or leaving her name. In our Web Analytics tool, Sally would be just another anonymous web visitor. Yes, with the web logs, we might know what city she came from and what web browser version she was using, and possibly even know whether she came from search or social media or other link, but Sally would still remain anonymous as a person and we would have no way of reaching out to contact her.
What is the submission rate for your lead generation web forms? Is it 5%? Typically up to 95% of the visitors to a website never submit their contact info and remain anonymous. And of the folks who do provide you their names and email addresses (hopefully real email addresses), 80% of them don’t even open your marketing emails. As a result, you can engage with only 1 out of 100 visitors via email.
At LinkedIn, our marketing teams use one of our own products, LinkedIn Lead Accelerator, to nurture anonymous visitors.
Here’s how LinkedIn Lead Accelerator works. When an anonymous visitor like Sally visits our marketing website, she gets tagged with a cookie.
Over the next four weeks, Lead Accelerator will orchestrate a sequence of online ads to be shown specifically to Sally via Sponsored Content on LinkedIn, ads on Facebook, and display ads elsewhere on the Internet. These nurture streams in Lead Accelerator can adjust to Sally’s actions in the meantime such as if she proceeded to view a case study. The marketer has an opportunity to share his company’s story with Sally even though Sally remains anonymous the whole time until she is ready to submit her contact info to our company.
Lead Accelerator drives more conversions for us and lets us measure the impact of our nurture programs with advanced analytics.
Lessons Learned about Customer Nurture
Send priority leads to sales with predictive lead scoring, otherwise nurture other leads
Consider using a product like LinkedIn Lead Accelerator to nurture anonymous visitors to convert into leads
Photo credit: http://tmblr.co/ZHTARn1cK8IZb
Now we have reached out to Sally, and have also nurtured her until she’s ready to engage with our sales team. I’m happy to say that she ends up purchasing our product and is onboarded as a new customer. How do we keep her as a happy customer though?
Customer churn is a challenge for all businesses everywhere. At this point, we want to focus on retaining Sally as a long-term repeat customer. For our enterprise sales channel, our Account Executives can periodically check in with Sally to make sure she’s happy with LinkedIn. However, what about our online self-service channel, where there’s neither a sales team nor a customer success team to provide a personal touch to retain the customer?
Next, we’ll explore what LinkedIn Marketing Solutions has done for customer retention for our online self-service business.
Photo credit: http://tmblr.co/ZHTARn1fdhKMf
For Enterprise customers, our sales team can build relationships with the customers, to help retain them as happy repeat customers.
However, what can you do to build happy online self-service customers without support from a sales team? Can you do it at scale?
Something we started in 2013 was the Red Carpet process where we would surprise, delight, and retain highest value self-service customers by sending them a gift basket with a handwritten thank you card, a printed best practices manual, and assorted chocolates and other treats. The entire fulfillment process was outsourced to a vendor under confidentiality and privacy agreements. From the LinkedIn side, the gift basket orders each week were automatically sent to the fulfillment vendor with a list of the new customers who met the spend threshold the prior week.
What was interesting about this Red Carpet process was how it blended the online and offline worlds. Giving welcome gifts to new customers has been a long time tradition by sales people from time immemorial. However, it’s unexpected for online businesses to do so. As part of the online world, we’re still able to measure the business impact of giving these welcome gifts to new customers who spent past a certain threshold amount.
We ran a random A/B split test of new high spend Text Ads self-service customers. Half of these customers received the surprise welcome gift basket shipped to them at the end of their first month, and the other half was the control group without the welcome gift. Normally, there’s a natural drop off of usage in the following 2nd month, as in the control group shown in blue here on the left, with a survival rate of 70%. With the surprise welcome gift shown in green here, the survival rate was 7% higher. Similarly for the customer spend metric, the number of customers continuing to spend more than $1000 per month was 87% higher with the customers receiving the surprise welcome gift basket. We were readily able to prove out ROI with this marketing program to demonstrate impact to customer retention.
It’s human nature to reciprocate. As we see here, when we delight new customers with a pleasant surprise welcome gift, customer retention rates improved.
Lessons Learned about Customer Retention
Delight your best customer segment with an unexpected surprise welcome gift for retention
Find ways to build customer relationships both online and offline for retention
Automate and outsource repeatable processes for scale
Use the analytics team to define the best customer segment and enable automation
Photo credit: http://tmblr.co/ZHTARn1cK8IZb
Now that we have retained our customers, our next customer lifecycle activity is to upsell to these customers. It may often be more cost effective to increase the spend of current customers than to acquire brand new customers. For the next example, we’ll take a look at upsell also within the self-service channel.
Photo credit: http://tmblr.co/ZHTARn1fdhKMf
There are over 7 million companies on LinkedIn. Each company can share status updates with their followers.
Some companies post status updates with great engagement, and others not so great.
As in this fictitious example, the marketers at the Antarctica Auto Insurance Company posted a feel-good article about their company’s sponsorship in a contest promoting teen safe driving in Antarctica. Their company status update received 31 likes and 4 comments. Not bad, eh?
The marketers at this insurance company have found value in the use of free company status updates to share among their followers. However, they haven’t yet used the Sponsored Updates advertising product to extend their reach and engagement of their content investment. With Sponsored Updates, they could potentially extend their message and brand quickly to their entire market in Antarctica.
The challenge for our LinkedIn marketers was to acquire more Sponsored Update customers like this insurance company. The Recommended Sponsored Updates marketing program was thus created. We wanted to send marketing emails to the customers who posted high engagement company status updates each week to recommend that they can promote that particular post, along with a $50 credit toward trying advertising using Sponsored Updates. The key feature of the marketing email was to show and highlight the status update that the company had posted and presumably proud of the engagement rate.
From the analytics team, we were involved in 3 steps in setting up this new marketing program.
Identify the best company status update for each potential customer. This had required extracting the status update with the highest engagement for each eligible company from among the billions of updates on LinkedIn each week, only feasible through a system like Hadoop.
Personalize the email creative. We needed to embed the top status update for each company in the marketing email. Initially in the pilot, we were programmatically taking screen captures of the rendered status updates from the web, but ran into scalability issues. Eventually our marketing operations team was able to incorporate the same CSS stylesheet look and feel from the LinkedIn site into our Responsys-based email marketing creative and we were able to fully scale out and automate the process with full personalization, fulfilling our 3rd objective which was to
Automate for scale.
Photo Credit: U.S. Geological Survey Department of the Interior/USGS
Photographer: William A Link Photographer Email: wlink@usgs.gov Photographer Organization: USGS
Photo by USGS scientist William A Link at Erebus Bay, Antarctica. Images were obtained under NMFS Permit No: 1032-1917.
http://gallery.usgs.gov/photos/03_17_2015_eja4Dpo00V_03_17_2015_23
As a result, we produced a marketing email that was worth at least $1.6 Million per year, based on estimates on the conservative side.
In this example, the insurance company marketer would receive an email with a message of “Hi Jeff, Your recent update got a 1.5% engagement rate – nice work! But why keep it to just your followers? Sponsor your update today to reach even more professionals in your target audience. Try Sponsored Updates today with $50 of free advertising!” Within the body of the email would be a reproduction of the company status update, personalized specifically for this individual email recipient who originally posted this company status update, including the thumbnail image of the post.
Currently, the LinkedIn Sponsored Update product has been the fastest growing product among the LinkedIn product portfolio across any business unit.
This email marketing program is one of our highest performing marketing emails as it targets the right person at the right time with the right offer to inspire potential with Recommended Sponsored Updates.
As this weekly process is automated, it’s pretty much hands-off as it runs by itself to upsell free company status update posters to the premium Sponsored Updates advertising product.
Credit: U.S. Geological Survey Department of the Interior/USGS
Photographer: William A Link Photographer Email: wlink@usgs.gov Photographer Organization: USGS
Photo by USGS scientist William A Link at Erebus Bay, Antarctica. Images were obtained under NMFS Permit No: 1032-1917.
http://gallery.usgs.gov/photos/03_17_2015_eja4Dpo00V_03_17_2015_23
Lessons Learned about Customer Upsell
Consider providing a free service to increase the user base for more potential upsell
Use data to personalize upsell message with:
Right person
Right timing
Right offer
Automate for scale
Photo credit: http://tmblr.co/ZHTARn1cK8IZb
Last we come to the 5th activity step in the Customer Lifecycle, which is to optimize. In some sense, optimization should be done throughout all the other steps as well. However, we’ll explore some general ways we optimize our marketing campaigns at LinkedIn, which result in generating even more ideas to refine future campaigns to attract new audiences with Reach again.
Photo credit: http://tmblr.co/ZHTARn1fdhKMf
We’re here today at the eMetrics Summit. A key activity that the practitioners here do would be to Optimize with Analytics. In order to optimize, I believe that we always need to be learning. When you combine both Human Learning and Machine Learning, then you can get Maximum Impact.
Human learning + Machine learning = Maximum Impact
We need a combination of good old-fashion human hunches and creativity, curiosity and experimentation along with the latest techniques in machine learning and analytics with big data to answer 3 fundamental business questions.
What has happened? Along with explanations why it happened.
What should you do?
What will happen?
For example, when we run a marketing campaign, we need to be able to tell if the campaign was successful or not in achieving its business objectives. What has happened? Which audience segments performed the best or the worst and why? Asking those questions and getting the deep insights will help us refine future marketing campaigns.
Once we know what has happened and why, then we can come up with a plan on what should we do next? It may require doubling down on a particular audience segment or marketing channel, or refining the propensity model.
However, it’s not enough to just come up with a revised plan. We also need to predict what will happen if we execute on our plan. For example, will the revised marketing campaign or propensity model achieve double the conversion rate? Only when we try to predict what our outcome will be would we be able to learn from it when we later go back to see how close our predictions were.
To enable Human Learning, a key basic requirement is to provide visibility into what has happened. Here is an example from a home grown marketing intelligence portal that we are developing for our marketing teams at LinkedIn called Nucleus (no relation to the Silicon Valley TV show).
Overall, these dashboards are essential in enabling closed loop performance optimization by informing humans what has happened and potentially what should be done.
These particular series of dashboards provide campaign level performance metrics which span across different channels such as marketing emails we send out using Eloqua and Responsys, combined with opportunity and closed won bookings data in our Salesforce CRM. Just integrating the data together from multiple sources, many of them in the Cloud, takes a bit of investment, but needs to be done.
The dashboards permit drill-down to performance by audience segments. We are also adding more proactive alerts into the portal to help the marketer focus on what’s urgent and important.
For developing this new particular marketing intelligence portal, we are using a combination of open source technologies such as PHP with Javascript and the Highcharts visualization library, along with some portions of the portal with dashboards embedded with Tableau, which is a commercial data visualization and dashboarding tool that we also commonly use at LinkedIn.
Despite all the buzz around predictive analytics, basic business intelligence is still essential to enable and scaling Human Learning. Like LinkedIn, you can use a commercial BI platform like Tableau or a home grown version or a combination. To be a data-driven marketer: either partner with engineering or IT to either build-or-buy necessary information systems, or else invest in the technical skills within your own group to implement scalable systems beyond basic Excel spreadsheets.
We talked about Human Learning via dashboards and alerts. Now, let’s take a look at how LinkedIn does optimizations using Machine Learning.
As an example of impact, from our online sourcing premium subscriptions business, we have seen Profile-based marketing (e.g. targeting by demographics like geography and industry and job function) accounts for 10% of the predictive power, and Signal-based marketing (targeting members who perform certain behavior steps) accounts for another 34% of the predictive power, and general propensity (which includes social data as well) accounts for 56% of the predictive power.
As you can see the propensity model-based marketing contributes to the overall predictive power the most.
In addition to targeting, having the propensity scores enables in new ideas in marketing campaigns. In this example, we are aiming for Personalized 1-to-1 Marketing. Both Steve and Emily have an overall propensity score of 0.9. However, Steve’s propensity score is driven by the behavioral score component—thus we send a related behavioral based message in the marketing email to him. On the other hand, Emily’s propensity score is driven by her social score component, and thus we send her a related marketing messaging regarding her social connections.
Analytics helps make personalized 1-to-1 marketing feasble.
Predictive models need maintenance and periodic tuning. Just like fresh fruit that is perfect at the beginning but rots over time, your predictive model will degrade over time as well unless it is re-calibrated, which can take a non-trivial amount of effort to do. What if you have several dozen or a hundred model that you need to maintain?
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Within our LinkedIn Business Analytics team, we have built an internal tool we call Modoop (which means Modeling on Hadoop). Modoop automates the building and tuning of predictive models. Since it enables repeatable processes, the models can be self-calibrating. We can even set up Modoop with alternate models and compete with them head-to-head in Champion-Challenger mode so that the better model will prevail each week
Incorporating automation in the Modoop model building and tuning frees up time for our staff to work on additional new areas to optimize.
Lessons Learned about Optimization
Human Learning + Machine Learning = Maximum Impact
More Data = More Power
Analytics enables 1:1 Marketing
Automate for scalability
I will leave with you some final thoughts. In the words of Edwards Deming, “In God we trust, all others must bring data.” To be an effective data driven marketer, we need to incorporate analytics in everything we do. Today, we looked at the Customer Lifecycle Activities of Reach, Nurture, Retain, Upsell, and Optimize at LinkedIn and how analytics plays a key part of those marketing processes. Although your specific company and industry may be different from LinkedIn, you still will have opportunities to use whatever you have and strengthen it with the power of analytics. I hope LinkedIn can also help you be productive and successful in your goals. Thank you for being a great audience. I will now take a few questions.