Big Data

Need a Chatbot Marketing Strategy? Start Here: Beginner’s Guide to Messenger Bots

When you think of chatbot marketing, what comes to mind?
Opinions run the gamut from fear — “What’ll it be like to entrust my customer service to a computer?” — to humor — “Here comes the robot apocalypse!” But reality is that there are marketing teams and support teams and sales teams making serious progress with their chatbot strategies.
We want to help you be one of those brands with a rockin’ chatbot strategy.
In this post, we’ll go deep into the world of messenger bots to give you the details on how to develop a best-in-class chatbot strategy. We’ll answer your questions about best practices for a nearly-human chatbot experience as well as how to get the most value out of chatbots on Facebook Messenger, Twitter, WhatsApp, and more.
Here we go!

First things first …
What is a chatbot?
A bot is simply a computer program that automates certain tasks.
In the case of a chatbot, the task being automated is a 1:1 conversation with a person.
These bots can use sophisticated technology like artificial intelligence and natural-language processing. Or they can be as simple as a series of IF-THEN statements. Alexa is a type of chatbot. So is the Domino’s Pizza app.
Today’s chatbots reply with text, yes, and also with audio, video, images, GIFs, you name it. Even the mediums for chatbots have grown exponentially — you’ve likely experienced bots in chat apps like Messenger and WhatsApp as well as on many, many websites with the little button in the corner asking if you need any help.
We’ll be focusing specifically on chatbots on social media channels in this post.
How to build your own messenger chatbot
The good news: You don’t have to have a lick of engineering skills.
There are a host of services that allow you to build your own chatbot, no engineering background required. A lot of these tools are as easy to set up as figuring out what you want your IF-THEN sequence to be:

Mobile Monkey

Facebook Messenger’s official page offers to build your own bot directly through the platform’s landing page. This method though, may be a little bit more complicated than others.

10 Tips for Your Chatbot Marketing Strategy
We ended up researching a number of different world-class chatbot experiences to learn from. Here is a list of 10 lessons for anyone about to get into chatbot marketing — like us.
1. Research your most frequently asked questions, by asking your team
One of the first things to consider with your bot is the content that it’ll contain.
Let’s take one of our favorite chatbot use cases as an example: a customer service bot. If the aim of the bot is to help customers and deliver speedy responses, then we suggest looking at the most frequently asked questions of your brand to see what content makes sense to start with.
To find these FAQs, there are a couple great places to look:

Your customer service team. They likely will have a heap of questions off the top of their head that they hear from customers all day.
Your social media support team, your community team, or your social media manager. Whoever does the engagement on your social profiles should have a good handle of common questions that come in through @mentions and DMs.
Your sales and marketing teams will have a pulse on what types of questions they see as customers progress through the funnel. This could look like common Sales questions that reps face, or it could be questions that your content marketers are seeking to answer.

And of course you could source questions from outside of your immediate team, too. The search suggestions at the bottom of relevant Google pages are a good place to start, as are crowdsourced communities like Quora and Reddit.
If you choose to build a bot for sales, lead gen, or any other service, we highly encourage you to research common questions and customer journeys so your bot is fully prepared to be as useful as possible.
2. Build your bot its very own conversation tree
Chatbots work best when given a concrete set of questions to answer. Without a certain level of specificity and pre-planning, then it becomes infinitely harder for a chatbot to deliver a believable experience — much less the right answer.
This is why a conversation tree works so well.
Picture a gigantic flowchart or a mindmap. Beginning with the initial hello from the bot and its very first ask of the user, you branch off from there, building the conversation flows for every different direction the conversation may turn.
We’re big fans of tools like Lucidcharts and Whimsical for creating easy-to-read flowcharts that would suit this type of project perfectly.
Mind map from Whimsical
3. Avoid fully open-ended conversations
Open-ended conversations can lead to confusion for your bot and a poor experience for the user. If you don’t have the luxury of highly-advanced language processing, then an open-ended question like “how can we help you today” could go any number of directions.
One of our favorite chatbots is the one at Hello Fresh in Facebook Messenger. Among the bot’s first messages to the user is an offering of a menu of choices: “Here are some common questions I can answer” Options include things like:

How does it work?
What does it cost?
Are you gluten-free?
Are you vegan?
Give me a discount!

The user can choose any of these statements by tapping on them in the Messenger interface. Then the bot will respond with an automated reply.
This takes the guesswork out of the bot’s replies since it knows exactly what to say to exactly which message it receives.
4. Let people know that a human is just a step away
One of the most interesting stats we’ve seen about chatbots is that people aren’t nearly as turned off by them as you’d think. 69% of consumers prefer communicating with chatbots versus in-app support. People love speedy answers to their problems.
That being said, that leaves 31% of consumers who might prefer the old-fashioned way — email or social support.
This can be baked into your bot experience, too.
Simply let people know as part of the bot’s welcome messages that the user is invited to get in touch with a human at anytime.
5. Give your bot a voice … and a warm welcome message
Just like you do with the way you write as your brand on social media, you’ll want to think about the voice and tone of your chatbot as well. Perhaps this is simply a natural extension of your brand’s voice and tone.
Other companies choose to lean into the “bot-ness” by making the voice a bit more obviously robotic.
Whatever you choose is entirely up to you. Just stay consistent with it throughout your conversation tree.
And one of the most important places to nail this voice and tone is in the opening message from your bot. We mentioned in the previous tip to be sure you let users know they can get in touch with a human at anytime. That’s a great nugget to place into your bot’s welcome message.
Also look to include things like:

A catchy hello.
Expectation-setting. Letting people know they’re talking with a bot.
And a solid first question with plenty of options to capture as many possible user journeys as you can.

6. Track the effectiveness of your bot with special UTMs and discount codes
One of the biggest questions you probably will have with your chatbot is … is this thing working?
And “working” can mean a lot of different things. If you’re using chatbots to minimize your customer support volume, then that’s an easy metric to check. If you’re wanting to measure the effectiveness of education, marketing, or sales, then it can be invaluable to track the bot’s success with measurable links and codes.
Hello Fresh does this with their bot — including the word “bot” right in their coupon codes (for example: FRESHBOT25).
Similarly, you can do this with your UTM codes for the content you link from your bot. Give it a UTM source of chatbot and you can measure the clicks and traffic that come from the bot, as well as track the UTM all the way through your customer journey.
You may even end up measuring ROI from the bot, which would be incredible!
7. Replace your email newsletters with chatbot newsletters
We’ve talked a lot about how great a chatbot can be for incoming requests. But how about outbound? There is a lot of room to experiment here. And one of the prime places is using your bot as a content delivery system.
For instance, on Facebook Messenger, any time someone talks to you through Messenger, they are added to your contact list. You can set up a chat bot to ask these folks to opt in to hear regular updates and announcements from you, then — voila! — you’ve just built a subscriber list on your messenger bot.
Tools like Mobile Monkey can then make it easy to send out new blog posts or quick information to this group. Some estimates say that chatbot newsletters generate a 70-80% engagement rate.
8. Send simple surveys to your contacts
With the high engagement rate with bots, you also have a good chance of getting your message noticed for surveys. It can be notoriously hard when surveying folks via email or on a website or app to get a high volume of responses. It’s a bit easier with bots.
Similar to the email newsletter tip above, with surveys, you first ask people to opt in to hear from you, then you can message them occasionally with a short and simple survey.
Many of the tools we mentioned earlier include the option for two button-based responses, which are perfectly suited for the mobile-first experiences of social media bots.
9. Enrich your bot with data and personalization
Check out this list of powerful chatbot superpowers:

Universal Studios tells you the wait time for rides
Marriott can tell you room availability
CheapFlights tells you the best options for your dates and your price range
Domino’s lets you order pizza!

These are all possible because of the Big Data that these brands pipe into their bots. If you’re not quite at this scale yet, no worries. You can dip your toe in the water by anticipating the most common questions of your customers and doing your best to fill in your bot with details. Simple things like hours of operations, daily deals, etc. can make for a delightful experience.
And if you do have a customer base who clamors for data-rich answers, then use the examples above to inspire your chatbot dreams.
There’s also the matter of personalization. And for this one, we’ll leave it up to your best judgment. Many tools allow you to personalize the chat experience with variables like first names or locations. This tows the line between helpful and offputting, when coming from a bot. Use discretion.
10. Make sure to promote your chatbot so people know you have one
This one might seem obvious, but it’s perhaps one of the most important tips we’ve covered so far. Your bot will only be successful if people find it and use it. So get the word out!
This can happen organically as people visit your Facebook page and are routed to you on Messenger. Or you can be proactive about it.
A couple of our favorite ways of promotion are:

Mentioning your bot in a list of all your customer support channels
Adding a call-to-action on your blog or website to get in touch with you
Advertising on social for people to opt-in to your bot experience. This can be great for the Messenger newsletters we talked about.

How The Top 21% Of PAM-Mature Enterprises Are Thwarting Privileged Credential Breaches

Energy, Technology & Finance are the most mature industries when it comes to Privileged Access Management (PAM) adoption and uses, outscoring peer industries by a wide margin.
58% of organizations do not use Multi-Factor Authentication (MFA) for privileged administrative access to servers, leaving their IT systems and infrastructure exposed to hacking attempts, including unchallenged privileged access abuse.
52% of organizations are using shared accounts for controlling privileged access, increasing the probability of privileged credential abuse.

These and many other fascinating insights are from the recently published Centrify 2019 Zero Trust Privilege Maturity Model Report created in partnership with Techvangelism. You can download a copy of the study here (PDF, 22 pp., no opt-in). Over 1,300 organizations participated in the survey from 11 industries with Technology, Finance, and Healthcare, comprising 50% of all organizations participating. Please see page 4 of the study for additional details regarding the methodology.
What makes this study noteworthy is that it’s the first of its kind to create a Zero Trust Privilege Maturity Model designed to help organizations better understand and define their ability to discover, protect, secure, manage, and provide privileged access. Also, this model can be used to help mature existing security implementations towards one that provides the greatest level of protection of identity, privileged access, and its use.
Key takeaways from the study include the following:

The top 21% of enterprises who excel at thwarting privileged credential breaches share a common set of attributes that differentiate them from their peers. Enterprises who most succeed at stopping security breaches have progressed beyond vault- and identity-centric techniques by hardening their environments through the use of centralized management of service and application accounts and enforcing host-based session, file, and process auditing. In short, the most secure organizations globally have reached a level of Privileged Access Management (PAM) maturity that reduces the probability of a breach successfully occurring due to privileged credential abuse.

Energy, Technology & Finance are the most mature industries adopting Privileged Access Management (PAM), outscoring peer industries by a wide margin. Government, Education, and Manufacturing are the industries most lagging in their adoption of Zero Trust Privilege (ZTP), making them the most vulnerable to breaches caused by privileged credential abuse. Education and Manufacturing are the most vulnerable industries of all, where it’s common for multiple manufacturing sites to use shared accounts for controlling privileged access. The study found shared accounts for controlling privileged access is commonplace, with 52% of all organizations reporting this occurring often. Presented below are the relative levels of Zero Trust Privilege Maturity by demographics, with the largest organizations having the most mature approaches to ZTP, which is expected given the size and scale of their IT and cybersecurity departments.

51% of organizations do not control access to transformational technologies with privileged access, including modern attack surfaces such as cloud workloads (38%), Big Data projects (65%), and containers (50%). Artificial Intelligence (AI)/Bots and Internet of Things (IoT) are two of the most vulnerable threat surfaces according to the 1,300 organizations surveyed. Just 16% of organizations have implemented a ZTP strategy to protect their AI/Bots technologies, and just 25% have implemented them for IoT. The graphic below compares usage or plans by transformational technologies.

58% of organizations aren’t using MFA for server login, and 25% have no plans for a password vault, two areas that are the first steps to defining a Privileged Access Management (PAM) strategy. Surprisingly, 26% do not use and do not plan to use MFA for server login, while approximately 32% do plan to use MFA for server logins. Organizations are missing out on opportunities to significantly harden their security posture by adopting password vaults and implementing MFA across all server logins. These two areas are essential for implementing a ZTP framework.

To minimize threats – both external and internal – Privileged Access Management needs to go beyond the fundamental gateway-based model and look to encompass host-enforced privileged access that addresses every means by which the organization leverages privileged credentials. With just 21% of organizations succeeding with mature Zero Trust Privilege deployments, 79% are vulnerable to privileged credential abuse-based breaches that are challenging to stop. Privileged credentials are the most trusted in an organization, allowing internal and external hackers the freedom to move throughout networks undetected. That’s why understanding where an organization is on the spectrum of ZTP maturity is so important, and why the findings from the Centrify and Techvangelism 2019 Zero Trust Privilege Maturity Model Report are worth noting and taking action on.

How to Create a Successful Business Intelligence (BI) Strategy for Your Agency

A well-crafted Business Intelligence strategy can bring clarity to your operations and improve decision-making. Learn how to create this strategy in this post.
Can you answer these questions without a moment’s hesitation?
What are your most profitable clients? What are your highest margin projects? How did your sales team perform in the last quarter?
If you’re like most agencies, you’d probably say “no”.
The Big Data revolution has transformed entire industries. The agency business, however, seems to be curiously unaffected by it. Scroll through any agency-focused publication and you’ll see article after article talking about creative skills. Data and analytics remain mostly a footnote, if at all.
You can blame this partly on the nature of the agency business itself. When your primary “product” is creativity, it’s tough to get people excited about something as objective as data.
It doesn’t help that wrangling useful data out of the systems agencies use isn’t always easy. Throw in a lack of executive buy-in and talent shortage and you have the present situation.
I’ll show you how to solve this data problem in this article. You’ll learn how to develop a sound business intelligence strategy, build a team, and get real insight from data.
Understanding Business Intelligence (BI)
Every business produces some data. It doesn’t matter whether you’re a two-person freelance team or a 500-person agency, if you are selling something, you’re also accumulating data related to it.
Think of a content marketing agency. In the course of its operations, this agency produces a lot of data, such as:

Marketing data related to its social media, website, and other marketing endeavors
Operational data related to its projects, resources, and day-to-day executive functions
Sales data related to the activities of its sales team, such as outbound calls, the opportunity to win ratio, etc.

Without a sound BI strategy, all this data would remain siloed. You might have traffic data from Google Analytics, but there would be no way for you to connect it to your operations or sales efforts. Worse, minus strong data intelligence, you can’t really track whether your performance matches your strategic vision.
The purpose of business intelligence is to turn this data into actionable insight.
In Gartner’s most recent survey of CIOs, BI/data analysis emerged as the number one concern for all respondents, significantly edging out more “buzzwordy” initiatives such as automation.

Before we proceed, you should know the difference between BI and Big Data. Though they sound similar, they differ greatly in impact, scope, and implementation.
Business intelligence differs from Big Data in that it is structured and repeatable. It draws in data regularly from pre-defined sources and makes it accessible and actionable.
Big Data, on the other hand, is where you use different processes to draw insight from large volumes of data. It is unstructured and non-repeatable. Two different data analysts can draw entirely different insight from the same data set.
Your Google Analytics dashboard is an example of business intelligence. It takes in data from pre-defined sources (such as your website) and turns it into actionable insight in real-time.

If you were to store all this raw visitor data as a CSV file and run a bunch of Excel formulas on it, you’d be doing ‘Big Data’.
The question now is: how do you go about creating a business intelligence strategy?
This is a four-step process, as I’ll show you below.
Creating a Business Intelligence Strategy
Although they might differ from business to business, the fundamentals of any business intelligence strategy remain the same.

Develop a vision and define your data requirements
Gather and store your data in a structured manner
Turn this data into an easily accessible visual data dashboard

As we’ll show you below, this is a multi-step process.
Step #1: Chart a BI Roadmap
You’re not going to get far with a BI initiative if you don’t have a clear agreement between stakeholders and a unified vision. In particular, there should be clear alignment between your BI strategy and corporate strategy.
Your first step, therefore, is to chart a BI roadmap. This is your overall strategy document outlining your vision, goals, and the path you’ll take to get there. It explains what’s in scope, what’s not, and the overall hierarchy of responsibility.
The BI roadmap should focus on the following:
1. Your corporate strategy
The purpose of any BI initiative is to give you insight into your business’ operations. For this to be successful, you have to first know what direction the business is moving into. Else, you might end up tracking metrics that have no real impact on the business’ long-term goals.
For instance, you might build a detailed marketing intelligence dashboard. But corporate might move to a direct sales and partnerships-focused model, rendering your marketing dashboard useless.
So the first order of business is to align the BI initiative with your corporate strategy. This isn’t easy, especially in larger organizations where executives don’t always like to share their long-term strategy.
Having a high-level stakeholder champion your cause can be effective. Get buy-in from someone on the board so you know what’s happening in the long-term and can change your BI focus accordingly.
2. Scope analysis
The final shape of your BI initiative will depend a lot on its scope. You’ll need a very different setup for gathering organization-wide operational metrics than say, just the marketing performance.
A scope analysis should be able to tell you what’s covered within the BI initiative and what’s not. Take the business apart and place everything into four categories:

Out-of-scope: Parts of the agency that are completely out-of-scope (for instance, HR and legal).
In-scope: Parts of the agency that are completely in-scope. These would be the core metrics for your BI dashboard.
Marginally in-scope: Parts of the agency that might be in-scope in the future, should circumstances change or the BI initiative pays off.
Temporarily in-scope: Parts that might be temporarily in-scope for short projects or reports.

3. KPIs and metrics
You can have great data, stunning visualizations, and substantial buy-in from stakeholders. But if you’re measuring the wrong metrics, your BI initiative will still be a failure.
This is particularly challenging for agencies where you might have countless seemingly similar metrics. For instance, it’s not always easy to tell whether you should measure overhead rate by % of the total bill or overhead rate by labor cost.

Having a tool that can quickly give you access to these related metrics can make your job much easier
Look at your data sources and divide everything into three categories:

Tracked metrics – Data that you will track regularly, but won’t use as a measure of performance.
Untracked metrics – Data that you won’t track. However, this data should be available for future analysis.
KPIs (Key Performance Indicators) – The metrics you will use to measure your performance. This would be a subset of your tracked metrics.

For instance, you might track average time on site, the number of pageviews per session, and bounce rate. But of these, you might choose only bounce rate as a KPI.
In addition to your own data, you might also want to keep track of industry-wide metrics to benchmark your performance.
4. Vision document
Finally, roll everything you’ve discovered so far into a vision document. This should be a short document outlining:

The broad goals of the BI initiative – what it aims to achieve, who all will be involved in it (more on this later), and what are its expected benefits
The long-term and short-term goals of the business and how a BI initiative can help you meet them
What’s in-scope and what’s not
What metrics you’ll track and how will you measure your performance

Get key stakeholders to sign-off on this document. Use it to evaluate the success or failure of your BI strategy.
This brings us to the next step in crafting a Business Intelligence strategy – managing stakeholders and BI teams.
Step #2: Assemble Your Team
BI initiatives are tough work. You need someone to provide data, someone to manage it, and someone to make sure that it shows what stakeholders want to see.
A lot of this depends on man-management and interpersonal relationships. But having a solid plan in place doesn’t hurt.
Here’s how you can go about assembling your own BI Avengers.
1. Understand stakeholder requirements
BI dashboards exist primarily for one reason: to help executives make better decisions. In fact, executive management remains the top driving force behind BI initiatives.

What if the dashboard isn’t even tracking the metrics executives care about? Or what if your data visualization and presentation just leave your stakeholders confused?
Understanding your stakeholder requirements, thus, is a crucial part of any BI strategy.
Zero in on the top stakeholders who will consume the data. For each of these stakeholders, figure out the following:

Visual preferences – How do they like to see their data (detailed vs broad overview, graphs vs hard numbers, etc.)
Priority metrics – What metrics do they care about the most? What metrics are most relevant to their department or area of expertise?
Business metrics – Outside of their core metrics, what metrics should they have priority access to for better decision-making? These should be from across the entire business, not just their concerned department.

2. Get access to data
To build your BI dashboard, you’ll need data from each department, and you’ll need it in an accessible format.
Getting access to this data isn’t always easy, especially in large agencies. Some departments might not have clear reporting standards. Others might not be willing to sign-off on their data to other departments. And some others will have concerns about the security of their data.
Your goal is to:

Convince stakeholders (especially department leaders) that their data will be safe. Using a tool like Workamajig that is certified can assuage a lot of concerns.
Persuade departments to adopt uniform reporting standards to make data analysis easier.
Get department heads to invest in reporting and data entry (something few people are interested in)

3. Build your BI team
How big (or small) your BI team is will depend on the scope of your BI initiative. Need a simple dashboard that shows you your marketing performance? You can probably saddle someone with this additional responsibility.
Need to gather data from across the organization? You’ll have multiple managers, developers, and analysts.
A full-fledged Business Intelligence team has the following roles:

Head of Business Intelligence: The person responsible for executing your BI strategy. Since she has to work closely with senior stakeholders, someone with strong personal relationships with them will thrive in this role. This is usually a VP-level position.
Business Analyst: The person responsible for acquiring data and turning it into insight. This is a core role in any BI team. A business analyst usually has a strong background in research and analysis, especially statistical analysis.
Developer: The developer builds integrations to put together data from multiple dashboards. While there are plenty of BYOD BI dashboards available, you might still need a full-time developer to make sure that all data is in the right format.

Outside of these roles, you might also have data scientists, especially if you deal with a ton of data and want to move towards Big Data, not just BI.
Of course, an easier alternative is to just use software like Workamajig that brings together data from across your agency in a single dashboard. This eliminates the need to reformat data or make sure that everyone is tracking the right metrics.
Once you have your team, what’s the next step?
Putting together your data, of course.
Step #3: Gather and Organize Your Data
Data is the fuel for any business intelligence strategy. You have to figure out a way to capture data, store it, and turn it into insight.
A sound data plan should be a core part of your BI strategy. Here’s what it should include:
1. Identify data sources
As an agency, you’ll have data from countless sources – clients, projects, sales, marketing, finance, etc.
The first step in your data plan, thus, is to identify all these data sources, their importance, and your integration plan for them.
There is no fixed process for how you organize your data. You can segregate them by department (finance, sales, etc.), by function (client acquisition, operations, etc.), or by business impact.
At the very least, you want to identify the most important metrics to your business (see the section above) and figure out how you will track them. For an agency, these might be client profitability, core marketing metrics, utilization rate, etc.
Given that building a dashboard is a top priority for most BI initiatives, it is crucial that you also start thinking of how your data sources will integrate into your dashboard.

An integration plan can make this process smoother, as we’ll see below.
2. Develop an integration plan
Once you’ve identified your data, you need a way to integrate it into a dashboard.
Depending on what tools you use, this might be one of the easiest parts of the process or the toughest. For instance, if you use modern SaaS tools almost exclusively, you’ll find that a lot of them integrate with popular BI tools such as Domo or Sisense.
On the other hand, if you use proprietary systems, on-premise software, or custom solutions, you’ll have to figure out how you’ll integrate your data with your BI dashboard. This might require creating a custom integration or even developing your own dashboard.
An easier solution is to use software like Workamajig to get ready access to all your data, from sales to accounting, is a ready-to-use dashboard.
3. Create a visualization strategy
Data isn’t useful if it isn’t presented in the right format. Your data plan, thus, should include a visualization strategy as well. This strategy should focus on:

What metrics you’ll prioritize in your dashboard, and
How you will visualize them

The answers to these questions will depend on your overall vision and stakeholder preferences. You might want to create multiple dashboards for different stakeholders. One stakeholder might prefer scatter graphs, while another might favor textual data.
I recommend working with a designer to make sure that your visualization strategy is easy to follow. Include specific details such as the types of graphs, colors, fonts, etc. you’ll use in the final dashboard.
Of course, make sure that your BI tool supports your visualization choices.
Over to You
Better data makes for better decision-making. A sound Business Intelligence strategy can make it much easier to gather your data and make it accessible to your agency’s decision-makers. Follow this three-step process to chart your BI strategy and bring clarity to your agency’s operations.