Ingredients: 125g butter, ¾ cup caster sugar, 1 teaspoon vanilla essence, 1 egg, 2 bananas (ripe & mashed), 1 ½ cups self-raising flour, and a ¼ cup of milk.
That’s what you need to make a banana cake.
You’ve also got to follow the recipe. But without the ingredients, anything else is irrelevant: No matter what you do, it just won’t be a banana cake.
Similarly, you need the right ingredients to make a lead magnet tasty enough for your prospects to eat up and come back for more.
The Lowdown on the Lead Magnet
Before we go on, let’s spell out what a lead magnet is: A lead magnet is an offer, incentive, or product intended to give your visitor value in return for their personal information (usually an email address or phone number).
A lead magnet is your metaphorical apple pie on a metaphorical window sill, metaphorical scent wafting out into the world and inviting people to take a closer look.
And if that apple pie turns out to be a tasty treat, then…
More and more of your “ideal customers” find you
More of those prospects will sign up or start a relationship, meaning more leads, deeper engagement (and ultimately, more sales)
Better brand awareness for your business as more people get to know you
A bigger and better reputation in the industry
The potential to “go viral,” with your lead magnet drawing a flood of leads
NEW YORK (Reuters) – PG&E Corp investor BlueMountain Capital Management LLC on Friday named 13 people it hopes to install as directors at the embattled power utility weeks after the company filed for bankruptcy in the wake of California’s catastrophic wildfires.
The hedge fund’s group of director nominees includes an expert in resolving victim claims, a former treasurer of the state of California, a prominent hedge fund manager, and people with banking and energy industry expertise.
BlueMountain, which owns roughly 8 million PG&E shares, in January announced plans for a proxy contest, criticizing the company for filing for Chapter 11 protection, a move it called unnecessary and harmful to investors.
The company and the hedge fund have been talking and last week agreed to extended the deadline to nominate directors to Friday.
PG&E said on Friday it has had a “constructive dialogue” with shareholders and stakeholders.
PG&E previously promised to make board changes, saying that only five of its current directors would stand for re-election at the May 21 annual meeting.
By offering to refresh its own board, PG&E could be trying to curry favor with big investors who may not be ready to back the hedge fund’s slate, analysts said.
BlueMountain’s slate includes former California Treasurer Phil Angelides who chaired the U.S. Financial Crisis Inquiry Commission to uncover the causes of the financial crisis and lawyer Kenneth Feinberg, known for administering compensation to victims including those of the Sept. 11 attacks.
Christopher Hart, a former chairman of the National Transportation Safety Board, Jeffrey Ubben, who founded $15 billion San Francisco-based hedge fund ValueAct Capital and is now focusing on sustainability, and clean energy expert David Crane, a former chief executive at NRG Energy, are also on the list.
California business and civic leaders Marjorie Bowen, a former Houlihan Lokey banker, and Alvaro Aguirre, a former banker and lawyer who has managed several corporate turnarounds, were also named.
The group also includes Fred Buckman, Donald Chappel, Tanuja Dehne, Dick Rosenblum, Mark Lerdal and Barbara Lloyd.
With a new board and fresh oversight, the hedge fund forecasted that the company’s share price could trade at $50 in the future. It closed at $17.03 on Thursday.
PG&E faces crushing liabilities related to deadly wildfires in 2017 and 2018 that killed dozens of people and destroyed thousands of homes.
Reporting by Svea Herbst-Bayliss; Editing by Phil Berlowitz
Most business leaders today are familiar with Scott Brinker’s famous Marketing Technology Landscape supergraphic:
In 2018, the graphic included 6,829 marketing technology solutions—almost double the number of solutions depicted in 2016. There are literally thousands of technology platforms at your fingertips. And each promises to solve every one of your business pain points.
But technology without purpose and strategy quickly becomes shelfware. A strategic approach to both sourcing and leveraging your marketing tools is essential.
Today’s post details the technology roadmap recommended by Widerfunnel’s Senior Experimentation Strategists. This roadmap highlights the “why” behind implementing certain technologies at certain moments in your organization’s experimentation journey. As well as how to get the most out of these tools by taking a strategic approach.
Note: We will not cover every type of marketing technology, but will focus on the tech stack that should be leveraged by a team that is conducting online experiments.
Strengthening the Technology pillar in your Experimentation Operating System
But first, let’s zoom out.
Your experimentation technology stack is just one piece of your Experimentation Operating System™ (EOS). There are four other pillars, which include: Process, Accountability, Culture, and Expertise.
You must view your tools within the context of the entire system:
How do they support you in documenting, unifying, disseminating, and automating your experimentation processes?
How do they support you to hold your experimentation program accountable—to properly track and report on program success?
How do they support you in collaborating with the organization at-large? In promoting visibility and transparency around experimentation to create a culture of experimentation?
How do they support your team of experts in terms of usability, productivity, training, and customer support?
How do they integrate and work with your existing technology stack?
As your experimentation program matures, you will likely experience constraints across the different pillars. Technology will not always be your most important constraint. In fact, we speak to many organizations with extremely sophisticated marketing technology stacks that are floundering in areas like process and culture.
Your experimentation program will only reach its growth-driving potential if it is strong across all pillars.
But today—we are talking technology. If this is your organization’s primary focus or suspected weak spot, you are reading the right blog post.
Technology in the Initiating Phase: Analytics & an experimentation platform
Organizations in the Initiating Phase of experimentation maturity are just getting started. In this stage, an Experimentation Champion is likely working to implement the appropriate technologies and get initial wins to prove the value of an experimentation program.
If your organization is just getting started with experimentation, your primary focuses should be:
Ensuring you have a solid analytics foundation, and
Identifying and selecting the experimentation platform for your needs
Analytics and quantitative research
Clean data for quantitative research is an essential starting point for any experimentation program.
Analytics data will inform the initial story of your customer journey; it will enable you to prioritize test areas and gain insight into your customers’ goals and intentions. We group these into two buckets: Primary analytics and supplementary analytics providers.
Primary Analytics: There are really two key players that provide primary analytics reporting for most organizations: Google Analytics and Adobe Analytics. Together, these two have a lock on around 75% of the market. Google dominates for websites of all sizes, however Adobe can’t be discounted, particularly as a solution amongst high-traffic Enterprises.
Supplementary Analytics: While Google and Adobe dominate as primary analytics solutions, there are many emerging technologies you can use to supplement the data your primary tool is collecting. These tools provide advanced solutions for data visualization, marketer-friendly tagging, and prescriptive analytics.
These solutions may not be worth the expense for smaller organizations in early stages of experimentation maturity. But when you start asking very complex questions and dealing with larger data-sets, your primary data analytics tool can become restrictive.
Companies that are looking to take their analytics game to the next level should look into:
With a solid analytics set-up, your team can start to uncover the low-hanging fruit within your digital experience and identify opportunities for testing. You will quickly find, however, that you need to implement an experimentation tool to increase the confidence you have in the changes you are making.
The next piece of the puzzle is a platform to facilitate experimentation. These tools allow you to measure the statistically valid effects of any change you make by limiting the impact of time and audience variables.
When you rely on analytics alone, you are relying on a less than accurate “before and after” measurement system. Making key business decisions based on this type of system can often lead you down the wrong path.
An experimentation tool allows you to run experiences simultaneously to randomized samples of your audience, giving you data you can trust to make decisions.
Before choosing a tool, it is important to identify your program’s requirements and parameters. For instance:
What sort of targeting requirements does your website have?
Optimizely is the market leader by market-share and is considered a thought leader in the experimentation space. They lead the way with their Stats Engine, broad integrations, and expansive product suite; they are considered a one-stop solution for organizations in any phase of experimentation maturity.
Optimizely’s experimentation-first perspective means they are usually first to market with the most innovative feature developments and advancements in the industry, keeping their customer base ahead of the competition. If you are an Enterprise organization, this is the first tool to evaluate. These features do come with a higher price tag though, and Optimizely may be out of range for smaller organizations that are just getting started.
Also servicing enterprise companies, Adobe Target provides a testing solution to those that are heavily leveraging the Adobe technology stack. Like Optimizely, Target offers solutions to most of the key requirements any company may have. Those with Adobe Analytics and other Adobe products may find additional value due to Adobe’s cross-product integration. However, Target isn’t the most user friendly tool. And if you aren’t a trained data analyst or statistician, its statistical model can leave room for error. If you don’t already leverage the Adobe suite, you will likely not get the full value of Adobe Target.
VWO is a great solution for smaller or mid-market companies. This tool has a much more friendly price-point and allows for all basic client-side testing functionality. They have a strong SmartStats Engine, and built-in variation heatmaps, which are a major bonus. VWO is a great option for organizations with a standard client-side website environment that are looking to prove the value of experimentation. However, companies with very specific technical needs may want to pass on VWO due to their lack of server-side or SPA technology.
New to the game (sort-of), technology giant Google has also developed an experimentation tool. The obvious advantage of Google Optimize is its native Google Analytics (GA) integration, and its free entry-level price-point. That said, the free tool is rudimentary and lacking in most advanced features. It also limits users in the number of experiments and metrics that can be in the tool at once. Optimize can be a worthwhile solution for GA organizations that are just getting started with basic testing.
Google’s paid version, Optimize 360, unlocks additional experiments and audience criteria that allow for more advanced experimentation. But it is only as valuable as your GA integration. If you have a team of GA experts, then you can do some creative analysis. However, Optimize 360 is likely not worth it for more entry level programs.
Keep in mind, Google Optimize is a sleeping giant. If Google decides to pour resources into the development of this tool, it could quickly become the dominant player in the space.
Loosely following the Theory of Evolution, Sentient lets you set thousands of variables and then watch as the tool “breeds” new combinations of variables and sends others to “extinction”. Eventually, it works its way towards the optimal combination. Due to its complexity, this solution is not for everyone. But in the right hands and on the right site you can cover a lot of ground quickly.
*Sites suited for MVT often have many fixed, modular components that are independent of one another.
Client-side versus server-side experimentation
One of the most important questions to consider at this stage is whether or not you need a client-side or server-side solution. Both have benefits.
Client-side execution means the code for your experiment variation will be injected through the browser. While this can be worse for performance, it enables the use of simple WYSIWYG interfaces that allow marketers to make changes without involving developers. One of the other major drawbacks is that you can only test on features that already exist in the DOM.
Server-side execution means that the variation code will actually run on your server. The primary benefits of server-side are improved performance, security, and the ability to test features that do not exist in the control environment. Teams can leverage this to roll out new pages, entirely new functionality and prototypes, or make changes on pages with complex server calls.
Server-side also greatly reduces the time needed to hardcode experiments because the code is already built in your native environment and follows all of your conventions. Server-side experimentation does require more initial investment to install the appropriate SDK but has efficiency and security benefits down the line.
Technology in the Building Phase: A foundation for collaboration
Organizations in the Building Phase of experimentation maturity are bought in on the value of experimentation. In this stage, an Experimentation Champion or team is likely establishing process and building the infrastructure to scale the program.
An analytics tool and an experimentation tool make up the technology stack of a basic Experimentation Operating System. A single team can leverage both to plan, run, and analyze experiments. However, as you work to scale your experimentation program, collaboration becomes crucial. How can you enable experimentation across many teams and business units?
While an individual can test with little documentation, your organization will need solid project management and record keeping to scale up an effective program. In our experience, you will need a specific experiment collaboration system in place to move upward on the experimentation maturity scale.
There are a few things to consider when selecting collaboration tools:
How will people throughout the organization submit test ideas?
Where will experiment wireframe and design files be hosted?
Where will people communicate about an experiment?
How will the current status of each experiment be displayed?
Where and how will the results be stored?
You may not need to source technologies that address all of these questions at the outset, but you should consider tool(s) that will be able to grow alongside your program. Here are a few popular solutions we have seen:
JIRA: JIRA can really do it all. Although its interface can be confusing at times, JIRA is extremely flexible, allowing you to build a custom process that works for most situations. JIRA is a strong solution if your experimentation program sits within Engineering or Product, since it is likely JIRA is already the tool of choice of your engineers. If your program sits within less technical teams, such as Marketing, you will likely want to turn to something a little bit more user friendly.
Asana: Asana is a strong task management solution, and is the tool of choice for project management at WiderFunnel. It allows users to build consistent templates, facilitates task assignment and communication, and has useful scheduling features. The accessible interface makes this a tool that everyone in the organization can use with ease.
Trello: Kanban boards! Certain organizations love working in a kanban view, and Trello is the leader in this space. For companies that want a visual representation of their experimentation projects, Trello can be a great solution. Plus, it integrates well with JIRA as both come from the same parent company—Atlassian.
Optimizely Program Management: One of the key value propositions offered by Optimizely is the integration with their Program Management solution. Optimizely Program Management allows users to store experiment ideas, vote on priority, track insights, report on program success, and more. Although pricey, Optimizely Program Management is a glimpse into the future of experimentation and insight management.
Traditional Spreadsheets: For teams in the early days of the Building Phase looking to get organized, spreadsheets are definitely a viable option. While spreadsheets often fall short on image storage and communication, they are a free, easy solution for smaller teams tracking a simple program. Keep in mind that scale will likely be a problem here.
As you add more tools to your marketing technology stack to enable experimentation, you’ll want to make sure they integrate nicely. If you’re an Adobe user, your analytics and testing tool will already be integrated—you should ensure that anything else you layer on plays nicely with this suite.
As an alternative to suite solutions, several leaders in the experimentation tech space have formed the Digital Experience Stack (DXS). If you use Optimizely or another tool within this stack, you will want to evaluate other DXS solutions.
Achieving experimentation maturity with qualitative research
Organizations in the Collaborating Phase of experimentation maturity are expanding the experimentation program and collaborating across teams. Finalizing a communications plan and overall protocol for the program is a priority here.
For organizations in the Scaling Phase of maturity, experimentation is a core strategy. Standards are in place and success metrics are aligned with overall business goals, enabling testing at scale.
Game-changing experiment ideas come from customer research. Quantitative input from analytics will help you identify potential pain points within your digital experience, but it only tells a portion of the story. Sophisticated experimentation programs also work to layer in qualitative research—an equally important counterpart that will help you fill in gaps in your understanding of your customers.
The goal with qualitative research is to uncover the “why” behind the “what” that you have observed with your quantitative tool.
Ideally, your experimentation team will have all of the following qualitative tools at their disposal:
User session recording
User Engagement Tools
Many organizations start with user engagement tools. These tools—including scrollmap, clickmap, heatmap, and user session recording features—help you visualize the visitor experience. They are useful because they are passive (requiring no additional action from your visitor) and are often low cost and easy to use.
When analyzing data from these tools, you’ll want to consider:
What % of your visitors is seeing specific important content?
What % of your visitors is scrolling past the fold?
Is there a particularly steep drop off after seeing a specific piece of content?
When comparing multiple calls-to-action, which are more commonly clicked?
What valuable information might your visitors be missing?
Where should you run your next test? (You may not want to test an element that few users are seeing)
Polls & Surveys
Polls and surveys take user research a step further by actually addressing specific questions to your audience. They give you the opportunity to ask questions while your user is in the shopping mindset and is evaluating your product. These require some action from users, but can often uncover much deeper insights (such as pain points within the customer journey you may have overlooked).
Exercise discretion with polls and surveys. Although they can provide rich customer feedback, they can also distract from your primary conversion goal.
Recommended tools to evaluate include:
Advanced experimentation & personalization: Data and customer management technologies
We hear shouts of “hyper-personalization” constantly—a one-to-one customer experience is the pinnacle for many organizations today. Of course, this relies on the existence of underlying data to define what makes an experience “personal”. Most organizations do not have the proper technology in place to enable this.
If your business has been struggling to do effective personalization, you should 1) interrogate your overall personalization strategy, and 2) look closely at how you are managing your customer data.
The proper use of a robust customer data platform (CDP) is a key component that differentiates mature experimentation programs from the immature. These platforms open up the world of audience management, boosting your ability to identify and target high value audience segments and plan test strategies around these groups.
Customer Data Platforms (CDP)
The CDP Institute defines a Customer Data Platform as “packaged software that creates a persistent, unified customer database that is accessible to other systems.”
A major benefit of a CDP is the ability to deliver a more effective customer experience and more impactful marketing messaging. (Which is really the goal with personalization). Your customers want a consistent experience across all of the channels and devices they’re using. They don’t want to see an ad for something they have already purchased. A CDP allows you to gain a complete view of your customer and deploy a consistent experience across touchpoints.
It is important to note, however, that a CDP is only as useful as it is comprehensive and actionable. This means that the number of data sources available to your CDP, as well as the number of execution integrations are both critical.
If you aren’t collecting data from multiple sources—website, mobile app, customer service system, in-store behavior, beacons, etc.—you may not be ready for a CDP. As well, if you can’t activate this data across multiple touchpoints—website, in-store, customer service interactions, etc.—you will not unlock the full potential of a CDP to provide a truly unified customer experience.
A CDP is not a personalization tool. However, it provides the data that will allow you to get the most use out of your personalization and experimentation tool.
If yours is a mature program and you are looking to enhance your personalization efforts or improve your overall data management efforts, Tealium’s Universal Data Hub is a great choice. Evergage is also a strong solution, combining a CDP with real-time personalization capabilities. If you already have a data platform in place, Evergage also functions as a powerful standalone personalization platform.
The business world has come a long way from the Mad Men era of focus groups and hunches. Today, technology enables experimentation at scale. It allows organizations to test one experience against another, achieving a statistically confident result. Which greatly reduces risk in decision-making.
However, it is vital that you define your experimentation strategy and ultimate objectives before sourcing your technology stack. Choose tools that fit these objectives, as well as your organization’s culture and the skill levels on your teams.
A final note: Before sourcing more tools, make sure you have a clear idea of what your program constraints really are. You can install every tool on the market, but that doesn’t mean you will have an effective experimentation program. First, you need a map of how you plan to get your program from A to B; technology is simply what you will use to pave the roads along the way.
What tools make up your marketing stack for experimentation? How do these tools support your overall experimentation efforts? Are your favorite tools on this list or did we miss them? Leave your thoughts in the comments section below!
Michael St Laurent
Director of Experimentation Strategy & Product Development Lead
Benchmark your experimentation maturity with our new 7-minute maturity assessment and get proven strategies to develop an insight-driving growth machine.
In a world where customers are bombarded across every possible channel with brand messages, targeting is more important than ever before. Small businesses need to be able to make their campaigns feel relevant and personal in order to keep up, but the processes involved – collecting, organizing and interpreting customer data to make it actionable – are often intimidating to small businesses and solo entrepreneurs with limited time and resources.
Collecting, organizing and learning from your customer data is critical no matter how large your team is or what stage of growth you’re in. In fact, there’s no better time to consider your processes for data than when you’re just starting out. And getting started with basic strategies for building customer relationships doesn’t have to be difficult – there are some simple steps you can take to save yourself a lot of time as your business grows and scales.
From the moment you start your business and establish an online presence, you should be laying the groundwork for effective CRM strategies. This includes: establishing a single-source of truth for your customer data, being thoughtful and organized about how you collect information and setting up the right processes to interpret that data and put it to work for your marketing. Here are some actionable steps (with examples) to take now:
Collect: Make sure you’re set up to onboard people who want to be marketed to. Whether you’re interacting online or in person, you should be collecting as many insights as possible (for example, adding a pop-up form to your website to capture visitors, or asking people about their specific interests when they sign up for your email list in store) and consolidating them so you can use them to market.
Organize: Once you have this data, make sure you’re organizing it in a way that will give you a complete picture of your customer, and make it easy to access the insights that are most important for your business to know. Creating a system where you can easily sort your contacts based on shared traits – such as geography, purchase behaviors or engagement levels – will make it much easier to target the right people with the right message.
Find insights: Find patterns in data that can spark new ideas for your marketing. For example, the realization that your most actively engaged customers are in the Pacific Northwest could lead to a themed campaign targeting this audience, a plan for a pop-up shop in that location or even just help you plan your email sends based on that time zone.
Take action: Turn insights into action, and automate to save time. As you learn more about your audience and what works for engaging them, make sure you’re making these insights scalable by setting up automations to trigger personalized messages based on different demographic or behavioral data.
Doing this right won’t just result in more personalized marketing campaigns and stronger, more loyal customer relationships – it will also help you be smart about where you focus your budget and resources as you continue to grow.
Opinions expressed in this article are those of the guest author and not necessarily Marketing Land. Staff authors are listed here.
About The Author
As VP of Marketing, Darcy Kurtz leads Mailchimp’s product marketing team. Her team aligns product strategy with marketing execution to make Mailchimp’s sophisticated marketing technology accessible for small businesses worldwide. Darcy joined Mailchimp with more than 25 years of experience leading global marketing at companies like Dell, Sage and Outsystems. She has a career-long passion for serving small businesses.
FILE PHOTO: An Apple logo is seen in a store in Los Angeles, California, U.S., March 24, 2017. REUTERS/Lucy Nicholson
(Reuters) – Apple Inc shareholders on Friday defeated a shareholder proposal from a conservative group that would have required the company to disclose the “ideological perspective” of nominees for its board of directors.
The proposal, placed on the shareholder ballot by the National Center for Public Policy Research, received only 1.7 percent of votes at the iPhone maker’s annual meeting in Cupertino, Calif.
The organization describes itself as a communications and research foundation supporting strong national defense and free market solutions.
Arguments were made both in favor of the proposal and against it in the Steve Jobs Theater, where the meeting is being held. Shareholder advisory firms Glass Lewis and Institutional Shareholder Services both recommended shareholders vote against it.
Reporting by Stephen Nellis; Editing by Bill Rigby
We’ve been working together with our sister site, ClickZ, to honor the best and brightest marketing technology companies today (which includes some SEO-related tools).
These Marketing Technology Awards are voted on 50/50 by the community and by a panel of judges. The ceremony will be hosted by Scott Brinker, and will take place on the night of March 21 in Tribeca, New York.
We’ve been raising quite a bit more hubbub about it on ClickZ, which more directly covers all marketing technology.
But since our SEW name is on there too, we wanted to make sure everyone here was in the loop as well. (You’ve probably seen it in the newsletters!)
Categories span across various types of marketing technology, including CDPs, ABMs, call analytics, conversational bots, and a dozen more.
And of course, a handful of more SEO type things such as search tools, location-based marketing, mobile marketing, etc.
Categories also include “Use of Technologies” (best campaigns, best tech stack), as well as “People” (martech CEO and CMO).
The awards were free to enter, and anyone who has used any of the platforms (excluding employees) could vote on them, rating the tools on things like ease of use, integration, innovation, value for money, customer service, etc.
Finalists were determined based 50% by community votes, and 50% by these judges.
Announcing the finalists
So for 2019, we want to thank everyone who has entered, nominated, voted, scored, and otherwise provided your valuable insights and experience.
We’d like to announce the list of finalists for this year, and offer a huge congratulations to everyone on this list.
We can’t wait to celebrate you and your great work at this event.
Here’s the full list:
Best Account Based Marketing Tool
Best Analytics Platform
Best Attribution Platform
Best Call Analytics Platform
Best Chat/Conversationsal Bot/Tool
Best Content Marketing Tool
Best Conversion Rate Optimization Tool/Technology
Best Customer Data Platform (CDP)
Arm Treasure Data
Best Customer Relationship Management Platform (CRM)
Best Data Privacy/GDPR Tool/Technology
Best Data Visualization Tool
Best Demand Side Platform (DSP)
The Trade Desk
Best Digital Asset Management Platform (DAM)
Best Email Service Provider (ESP)
Best Influencer Marketing Platform
IZEA Worldwide Inc.
Best Location Based Marketing Platform
Best Marketing Automation Platform (MAP)
Best Mobile Marketing Platform
Best Paid Media/Bid Management Tool
Kenshoo (Kenshoo Search)
Best Personalization Platform
Best Predictive Analytics Platform
Keen Decision Systems
Best Sales Enablement Technology
List Partners LLC
Best SEO Tool
Best Social Media Marketing & Monitoring Company
Kenshoo (Kenshoo Social)
Overall – Marketing Technology Company of the Year
To be announced from the list of finalist at the awards dinner
Use of technologies
Best Customer Experience Campaign
Nestlé (Nestlé China)
Ogilvy (H&M & Ogilvy)
Best Data Enablement Campaign
Catalyst (Catalyst & Tauck)
Idomoo (Fairmont Hotels & Resorts)
Marketing Evolution (Marketing Evolution)
Best Marketing Technology Stack
Best Personalization Campaign
Conversant (Swanson Health)
Location3 (Mountain Mike’s Pizza)
Selligent Marketing Cloud (OPEL NETHERLANDS)
Sitecore (Herschend Family Entertainment (Dollywood.com))
Velocity Worldwide (The Belfast Classic/Sport Changes Life)
Best Technology Combination
Merkle (Globe Life and Accident Insurance)
Best Use of Marketing Technology
Adobe (Adobe & Adobe Advertising Cloud)
Ogilvy (H&M & Ogilvy)
SAP (SAP & MSIGHTS, Inc.)
Marketing Technology CEO Award
Conductor (Seth Besmertnik, CEO, Conductor)
Marketing Evolution (Rex Briggs, CEO, Marketing Evolution)
Get top insights and news from our search experts.
7 expert strategies on Amazon Advertising, and how to use AMS and organic impressions to get your brand seen. Tips presented by John Denny of Cavu Venture Partners (formerly Bai) and Luis Navarrete Gomez of LEGO at the Transformation of Search Summit.
Featured snippets from the Transformation of Search Summit held in NY on Oct 19, 2018. Key highlights and quotes from the full day of speakers and sessions.
New research, The Era of Ecommerce, released today in partnership with Catalyst, finds disconnect between consumer behavior and advertiser spend online.
Consumers are ignoring the mass of email and seeking out businesses on their own terms — when and where they want to look. Search marketing gets you found.
Want to stay on top of the latest search trends?
Get top insights and news from our search experts.
WASHINGTON (Reuters) – U.S. personal income fell for the first time in more than three years in January as dividends and interest payments dropped, pointing to moderate growth in consumer spending after it fell by the most since 2009 in December.
FILE PHOTO: A customer pays her lunch bill at the Other Side Cafe in Boston, Massachusetts in this file photo taken on October 1, 2009. REUTERS/Jessica Rinaldi/Files
The report from the Commerce Department on Friday also showed inflation pressures remaining tame, which together with slowing domestic and global economic growth gave more credence to the Federal Reserve’s “patient” stance towards raising interest rates further this year.
Personal income slipped 0.1 percent in January, the first decline since November 2015, after jumping 1.0 percent in December. Income was weighed down by decreases in dividend, farm proprietors’ and interest income. Wages increased by a moderate 0.3 percent in January after rising 0.5 percent in December.
Economists polled by Reuters had forecast incomes rising 0.3 percent in January.
The Commerce Department did not publish the January consumer spending portion of the report as the collection and processing of retail sales data was delayed by a 35-day partial shutdown of the government that ended on Jan. 25.
It reported that consumer spending, which accounts for more than two-thirds of U.S. economic activity, dropped 0.5 percent in December. That was the biggest decline since September 2009 and followed a 0.6 percent increase in November.
Households cut back on purchases of motor vehicles and recreational goods in December, leading to a 1.9 percent plunge in spending on goods. Spending on goods increased 1.0 percent in November. Outlays on services edged up 0.1 percent, held back by a decline in spending on household electricity and gas. Spending on services advanced 0.4 percent in November.
When adjusted for inflation, consumer spending fell 0.6 percent in December, also the largest drop since September 2009, after rising 0.5 percent in November.
The December data was included in the fourth-quarter gross domestic product report published on Thursday, which showed consumer spending growing at a 2.8 percent annualized rate during that period, slower than the third quarter’s robust 3.5 percent pace. The economy grew at a 2.6 percent rate in the October-December quarter after notching a 3.4 percent pace in the third quarter.
U.S. financial markets were little moved by the data.
The sharp deceleration in consumer spending in December puts consumption on a lower growth trajectory in the first quarter and bolsters analysts’ expectations that the economy will slow down further in the first three months of the year.
Still, consumer spending likely remains supported by an accumulation of savings. In December, savings increased to $1.2 trillion, the highest level since December 2012, from $961.3 billion in November. The saving rate jumped to a three-year high of 7.6 percent.
The economy is losing momentum as the boost from a $1.5 trillion tax cut package and increased government spending fades.
FILE PHOTO: United States one dollar bills on a light table at the Bureau of Engraving and Printing in Washington Nov. 14, 2014. REUTERS Gary Cameron/File Photo/File Photo
A trade war between the United States and China, higher interest rates, softening global growth as well as uncertainty over Britain’s exit from the European Union are also negatively affecting the economy.
Inflation was benign in December. The personal consumption expenditures (PCE) price index excluding the volatile food and energy components rose 0.2 percent after a similar gain in November. That left the year-on-year increase in the so-called core PCE price index at 1.9 percent.
The core PCE index is the Fed’s preferred inflation measure. It hit the U.S. central bank’s 2 percent inflation target in March for the first time since April 2012.
Reporting by Lucia Mutikani; Editing by Andrea Ricci
Company: The Motley Fool Industry: Media; Financial Services Business Model: Premium Service Subscription Experimentation Champion: Nate Wallingsford, Head of U.S. Marketing Operations and Optimization at The Motley Fool
Increase paid user acquisition to hit revenue targets
Increase user engagement with premium services
Increase member retention
Increase customer lifetime value
Process-oriented experimentation featuring proper Design of Experiments
Collaborative experiment ideation between The Motley Fool and WiderFunnel
Internal socialization of experimentation at The Motley Fool
Staff augmentation (UX / UI Design and Web Development)
Millions of dollars in uncovered additional annual revenue
Key customer insights generated and leveraged throughout The Motley Fool’s funnel
Unified experimentation efforts across The Motley Fool’s marketing and product teams
Controlled experiments can transform decision making into a scientific, evidence-driven process—rather than an intuitive reaction. Without them, many breakthroughs might never happen, and many bad ideas would be implemented, only to fail, wasting resources.
The full story: How The Motley Fool uses experimentation to drive growth
When Nate Wallingsford stepped into the role of Director of Conversions, Acquisition Marketing at The Motley Fool 3 years ago, he knew that experimentation would be a primary focus.
Nate understood that the ability to provide data-backed recommendations for digital experience improvements is crucial. And for a multimedia company like The Motley Fool, whose website receives millions of unique visitors each month, even a slight increase in customer acquisition rate could have a massive impact on the bottom-line.
Today, Nate is the Head of US Marketing Operations and Optimization at The Motley Fool, and the company is testing across the entire customer journey and multiple teams.
While experimentation has always been celebrated at The Motley Fool, Nate’s work in partnership with WiderFunnel has led to increased visibility for the experimentation program, massive revenue gains, and—perhaps most importantly—actionable customer insights.
This case study explores how Nate and his team are using experimentation as a fool-proof (pun intended) growth strategy.
The business context
As mentioned, The Motley Fool is an established multimedia financial services company.
Their website consists of pages on pages of useful, free content for investors; the company generates revenue through various stock, investing, and personal finance premium services. The Motley Fool’s funnel consists of three primary areas:
User acquisition or front-end marketing: The goal being to encourage non-paying members to sign up and become paying members
Upsell or back-end marketing: The goal being to encourage members paying for lower-tier services to purchase more premium services
Product: The goal is to increase member engagement with the service(s) they’re paying for and increase member retention
Each area of the funnel is the responsibility of a different team within The Motley Fool, but everyone is focused on creating the best customer experience and increasing customer lifetime value.
Across the teams, we’re all really focused on customer lifetime value (LTV) for every stage. Whether it’s someone’s first product with us—getting them into a higher tier. And on the product side, the more we get people to renew, the higher their lifetime value. It’s a win-win for both the customer and us—they get great stock advice and recommendations and we get to retain them as a customer.
— Nate Wallingsford, Head of US Marketing Operations & Optimization at The Motley Fool
As the leader of the user acquisition team, Nate’s main focus was to hit his revenue targets by increasing paid subscriptions. And experimentation was his tool of choice.
Finding the right experimentation partner
From the outset, Nate had almost everything he needed to build a successful experimentation program within his team: senior-level support, high website traffic, plenty of ideas for improvement. But he was lacking a process.
Nate was running what he refers to as “good-idea tests” – you have what you think is a good idea, you test it, it wins, loses or is inconclusive. If it wins, hooray! You can implement changes. But if it loses, it’s back to the drawing board.
As a self-proclaimed process-oriented person, he knew there had to be a better method. It was in this moment—searching for experimentation processes on Google—that Nate stumbled upon WiderFunnel Founder Chris Goward’s book, You Should Test That! He bought it, read it, and loved it.
I thought, ‘This book is awesome.’ I’m going to start building out our optimization program for acquisition around the WiderFunnel methodologies.
Nate understood experimentation from day one. It was fantastic. He was already familiar with the LIFT Model and experiment design principles. We were able to jump into collaborative experiment ideation and strategy right away. You know, the fun stuff.
Ultimately, Nate decided to partner with WiderFunnel to ensure his program would be as successful as possible: To solidify his use of these processes internally. To gain access to additional design and web development resources. And to collaborate with expert experimentation strategists who are testing across industries every day.
The power of fresh perspective
With every new client, there is a discovery phase. In the early days of our partnership, Nate and the WiderFunnel team dug into The Motley Fool’s most important user acquisition goals and conducted initial analyses of fool.com.
One of the first things we noticed was the lack of a prominent call-to-action to sign up for The Motley Fool’s premium services. Users were landing on the site and engaging with content, but there was no clear path to conversion. In fact, there was almost no indication that there were professional services available.
Nate and his team have a ton of knowledge about their product, their marketing, and their customer, but they had overlooked a seemingly common-sense improvement. This happens to marketers constantly. We are so close to our day-to-day, so wrapped up in our way of thinking, that we miss simple solutions.
Which is why a partner with a fresh perspective can be essential.
The first experiment WiderFunnel ran with us was to add a call-to-action button to our desktop site-wide navigation. It was super simple and seemed like a no-brainer, but the impact was insane. After that, I knew this was a good decision. And I knew it was going to be really cool to work with [WiderFunnel].
— Nate Wallingsford
The experiment details
For this experiment, we added a “Latest Stock Picks” call-to-action in the navigation. This CTA replaced a dropdown menu labelled “Stock Picks”. The assumption was that Motley Fool users are looking for stock-picking advice, specifically.
Hypothesis: Creating a clear “Latest Stock Picks” CTA in the site-wide navigation will cause more users to enter a revenue-driving funnel from all areas of fool.com.
Two variations were tested. Each featured the “Latest Stock Picks” call-to-action. But in each variation, this CTA took the user to a different page. Our ultimate goal was to find out:
If users were even aware that there were premium paid services offered, and
Which funnel is best to help users make a decision and, ultimately, a purchase
In variation A, the “Latest Stock Picks” call-to-action sent users to the homepage and anchored them in the premium services section. This section provides detail about The Motley Fool’s different offerings, along with a “Sign Up Today” call-to-action.
With variation B, we wanted to experiment with limiting choice. Rather than showing users several product options, the “Latest Stock Picks” call-to-action sent them directly to the Stock Advisor service sign up page; this was Motley Fool’s most popular service.
Both variations beat the control. Variation A resulted in an 11.2% lift in transactions with 99% confidence and variation B resulted in a 7.9% increase in transactions with 97% confidence.
Interestingly, because variation B was built on variation A using factorial design, we were able to see that this change actually decreased transactions by 3.3%.
What is Factorial Experiment Design?
Factorial Design is a method of Design of Experiments. Similar to multivariate (MVT) testing, factorial design allows you to test more than one element change within the same variation. The greatest difference is that factorial design doesn’t force you to test every possible combination of changes.
Instead of creating a variation for every combination of changed elements, you can design an experiment to focus on specific isolations that you hypothesize will have the biggest impact or drives insights.
In The Motley Fool’s initial experience, users may have been unsure of how to sign up (or that they could sign up) due to lack of call-to-action prominence on the original site-wide navigation. Users also seemed to prefer some degree of choice over being sent to one product (as seen with the decrease in transactions caused by variation B).
Following the customer insights
One of the biggest problems with the “good idea” testing that Nate had been doing is that it prioritizes conversion rate lift over insights, over learning. If an experiment won, great. If not, it had zero value. All that effort to ideate, design, and run the test was wasted.
But a great experimentation program will generate insights with every single test. And that’s what Nate began to build with WiderFunnel.
One series of experiments was particularly fascinating. It generated a core customer insight that The Motley Fool is still leveraging and validating throughout their digital experience. The initial idea was to leverage an extremely common persuasion principle: social proof. Adding social proof to any customer experience is widely accepted as a “CRO best practice”.
The experiment details
A secondary top-of-funnel metric for The Motley Fool is email sign-ups for the company’s email marketing list. For the first experiment on this funnel, we focused on the email capture modal. In one of our variations, we added a social proof statement: “Join over 121,837,512 other Fools who have come to The Motley Fool for investing insights.”
This tactic has worked for other WiderFunnel clients in the past, encouraging more users to enter a revenue-driving funnel. In this case, however, the variation tanked. It resulted in a -11.2% decrease in sign-ups.
In this experiment, social proof resulted in an extreme reaction from users, indicating high sensitivity around this persuasion technique. One theory was as follows: Rather than being comforted by the fact that others trust The Motley Fool, prospective customers may actually be looking for exclusivity.
Nate and WiderFunnel Strategist, James Flory, wanted to understand further. They ran another experiment, this time on the primary landing page for the email capture funnel. But they leveraged a different form of social proof: a customer testimonial.
The Hypothesis: Adding testimonials will make users trust this page as a place to submit their emails and improve email capture rates.
Again, Motley Fool users responded negatively. The social proof variation resulted in a slight decrease in conversions. Seeing this result, Nate thought about the testimonials splashed across the customer journey and wondered: Should we remove social proof throughout the funnel?
“We had started to test injecting social proof into our lead capture pages. And we saw a drag on conversions any time we did that. We tried adding social proof and trust elements on our video sales letter pages. That had a drag on conversion. And we thought it was strange. Adding social proof seems like a best practice, an industry standard,” explains Nate.
“So, we thought, let’s experiment with this on our order pages. These are at the bottom of the funnel, right before purchase. What happens if we remove the testimonials and social proof we have on order pages? Will we then see a lift because earlier in the funnel we saw a drag across the board?”
We ran another experiment, this time on the order page—the stage of the funnel that includes the point of purchase. In the variation, all customer testimonials were removed. This variation performed terribly, decreasing transactions, average order value and revenue per session. However, it generated several actionable insights:
Previous learnings indicated social proof had a negative correlation with conversion rate. This experiment challenged that insight.
It may be that, in the early stages of the user journey, users are not yet in a purchase state of mind and still crave exclusivity.
Early stages of the funnel don’t hint at a paid service or subscription, but adding testimonials may put the thought of an upcoming sales pitch into the user’s mind, possibly triggering an exit or increased wariness.
Inversely, when a user is exposed to a purchase decision, they respond positively to social proof which may reduce anxiety and increase trust and confidence in their decision.
That was really interesting to see. Even though we had a decrease in conversion rates across all three experiments, they generated this insight that social proof and testimonials are huge at the point of purchase, but may need to be avoided at the top of the funnel.
— Nate Wallingsford
This series of experiments points to the importance of experimentation in general. If Nate had simply made changes to fool.com based on best practices, he might have seen conversion rates drop with no understanding as to why.
And if he hadn’t been leveraging an experimentation process to understand where to retest and revalidate insights (in this case, the threshold and elasticity of social proof), he might’ve just removed social proof lower in the funnel based on the initial experiment results, assuming that social proof doesn’t work.
Socializing experimentation: The importance of gaining visibility
Every marketer and product owner has growth targets they are trying to hit. Which is why achieving positive experiment results is hugely important. But visibility is crucial to the longevity of any experimentation program—on both winning experiments and ‘losing experiments’ that generate learnings.
Nate’s goal has always been to promote a culture of evidence-based decision-making at The Motley Fool.
Early on, Nate realized that the insights gained through process-based experimentation were a firestarter for even better tests. He wanted to spread this knowledge throughout the organization, so he began compiling his experiments and insights into a monthly email newsletter.
At first, Nate was just distributing this newsletter to the U.S. acquisition team. But people began to forward it on, and more Fools became interested in joining his distribution list. So, he began to scale this communication to other teams.
This newsletter became a key resource for other teams at The Motley Fool—specifically teams with lower website traffic. These teams lack the traffic volume to test at the same velocity as the acquisition team, but are able to leverage Nate’s insights and results to implement new experiences on their sites.
Today, Nate and his colleague Lauren conduct a weekly standup on experimentation. Attendees come from across the company—from marketing, technical, and editorial teams. This constant communication generates buzz and momentum around experimentation at The Motley Fool and is a key piece of Nate’s strategy.
The future of experimentation at the Motley Fool
At the beginning of this partnership, Nate was looking to leverage WiderFunnel’s expertise in experimentation and augment his resources to scale The Motley Fool’s experimentation program quickly. The relationship has since morphed into a highly collaborative partnership. Today, Nate and James feed off each other’s insights and ideas to develop new tests and experiences together.
The test ideation, optimization conversations, and overall rapport [between us and WiderFunnel] is exceptional. I feel like I’m having these conversations with my colleagues, not an agency.
— Nate Wallingsford
Recently, WiderFunnel and The Motley Fool expanded their partnership to help drive testing strategy within The Motley Fool’s product experience. This aligns perfectly with Nate’s priorities for experimentation, which are:
To enable deeper collaboration between the Marketing and Product teams and unify the new member journey from purchase to product experience
And to optimize The Motley Fool’s mobile experience and improve monetization
“Things change quickly at The Motley Fool,” explains Nate. “I always try to prioritize the experiments that have the biggest potential business impact. It’s a big part of what has made our program successful, and will be a continued focus for our team.”
Watch this on-demand webinar and discover how Nate and his team are leveraging experimentation to uncover massive revenue gains and actionable customer insights. And learn how Nate has worked to gain visibility and create excitement around testing.