Today on the Demand Gen Pod, Episode 11,
Summary notes from Episode 11:
Intent data can provide valuable insights and boost conversion rates in
marketing and sales. Accurate and complete data is crucial for intelligent
customer engagement and personalization. Data accuracy and completeness can be
ensured through data validation, quality controls, regular audits, and data
enrichment. Data integration is important for connecting and accessing data
across different systems and departments. Challenges can arise from siloed
data and data duplication. Data integration and accessibility can be improved
through integration technologies, API connections, and centralized data
platforms. Having a master platform, such as Salesforce, as the source of
truth is important for data consistency. Data cleansing and standardization
improve data quality and customer experiences. Strategies for data cleansing
and standardization include data profiling, deduping, validation rules, and
data normalization techniques. A robust data strategy and governance framework
is essential for structured data management.
Full Transcript:
00:01
Welcome to demand gen pod. Today, get ready to unlock the power of intent data in marketing and sales. In this episode, we’re going to delve into the world of intent data to explore how businesses can harness its potential to gain valuable insights, drive personalization, and boost conversion rates. Join us as we uncover the different types of intent data, discuss practical applications in marketing and sales, and highlight some of the impacts that intent data can have on particularly account based marketing strategies. From understanding customer behavior to prioritizing leads and measuring success, we will cover it all. So if you’re ready to tap into the immense opportunities that intent data offers, stay tuned. Let’s get started on the demand gen pod. First, let’s start with data accuracy and completeness.
00:48
Accurate and complete data is as important for intelligent customer engagement because it ensures reliable insights and enables personalization. If your data is crap, the output will be crap. Input crap, output crap. Pretty straightforward, but using inaccurate data or incomplete data in a customer engagement strategy can lead to wrong decisions, wasted resources, poor customer experiences, and damaged reputations. And businesses can ensure their data accuracy and completeness by validating data sources, implementing data quality controls, conducting regular audits, and filling gaps through data enrichment. Now, data integration impacts data readiness by ensuring data is connected, consistent and accessible across different systems and departments. Whether you’re using Salesforce or HubSpot, whatever your CRM tool is, having that connected to everything else that you’re using in marketing and sales is so important. And challenges can arise when data isn’t easily accessible or integrated, like siloed data or daily duplication.
01:45
So to improve data integration and accessibility, you can easily invest integration technologies, establish API connections and integration standards, and break down data silos through centralized data platforms. Salesforce is an incredibly powerful tool to accomplish this, but I think that one other thing that’s really important here is that you have a takeaway and you have a standard that you use and one platform that you consider the master platform. Okay, so if data Salesforce is your master platform, it’s the source of truth. Sometimes we say, then if you’re ever confused about what you’re seeing somewhere else, you would always go back to that source of truth. For larger companies, the reality is you might have several different sources of truths depending on what that data is and what it’s showing. So that’s something else to keep in mind. What about cleansing and standardization?
02:34
So data cleansing and standardization are essential for intelligent customer engagement because they improve that data quality, consistency and reliability. Using unclean or unstandardized data can result in accurate highlights, inconsistent experience and can kind of piss your customers off if you’re calling them the wrong name. For example, strategies and tools for data cleansing standardization can be such as like data profiling, deduping data to validation rules and data normalization techniques. And what do I mean by that? So for example, you could simplify things like data birth for people. When you have somebody come in, you could have names come in from Salesforce, which might be in all capital letters. You could have a program which would clean that up for you so that it would make it into basically title case, right? So capital, first name, capital, last name, phone numbers.
03:23
You could clean up phone numbers so that no matter how somebody put a phone number in Salesforce, it would always have a clean phone number on the output. So it always looks nice. It’s not one five, it’s one slash, five, or hyphen or whatever. The, I guess hyphen would make more sense. So you can definitely do all of those things as well. Now, what about data strategy and governance? So having a robust data strategy and governance framework ensures the data readiness by providing that structured approach to data management and utilization. And some key elements here are going to be clear objectives for your data science team, clear governance policies, data architecture, data quality management and data lifecycle management. So are you keeping track of who is in the system? Who is leaving the system?
04:12
If somebody gets a new email address, are they getting merged into their old profile? Businesses can establish effective data governance practices by assigning data ownership and establishing data governance roles and responsibilities, and then implementing those tools that you can get access to and conducting regular data governance audits, even quarterly. It’s so important, but data changes all the time, particularly in b to B. B to C might not be quite as prevalent, but b to B, absolutely. What about some data analytics and insights? So analytics contributes to the readiness by data analytics contributes to the readiness by enabling businesses to derive actionable insights from their data and make data driven decisions. Common challenges in deriving actionable insights include data complexity, data silos, inadequate analytics tools, and a lack of skilled resources.
05:01
All of these are really important to get right, but techniques and tools for analyzing and extracting insights from data can include things like data visualization, machine learning algorithms, predictive analytics, and customer segmentation. You can use things like power BI, for example, or tableau to help you with that. Data readiness assessments are also really important, so businesses can assess the readiness of their data for intelligent engagement by conducting those data audits, reviewing data metrics, and accessing data integration capabilities and key indicators or metrics to look for when evaluating data readiness include data accuracy, completeness, consistency, accessibility, integration, and compliance. Some actions that businesses can take to improve overall data readiness working on implementing data quality improvement initiatives, investing in data integration technologies, establishing those governance policies, and providing data literacy training for employees.
06:00
So some big takeaways, this is a quick one today, but some big takeaways. Data readiness, it’s so important. It’s so important for intelligent customer engagement because it allows you to make informed decisions, personalize your experiences for each individual customer, and drive customer satisfaction. Now, how can you actually use this, right? So you can use things like date of birth. You can use things like last purchase, what they purchased, websites that have been visited, gender, particular preferences or favorites. Any of these things that you collect can be leveraged as data points. The important thing is that you are sure that you’re collecting them in a way which is consistently either updated or data points that don’t necessarily change very frequently, and that you’re actually using them right.
06:56
So if you understand something about your customer, whether it’s the last thing that they purchased or when they purchased, you might know, for example, when the warranty expires on their product, you could offer warranty expiration warnings saying, hey, the warranty on your product is about to expire. You may want to consider upping and getting a new one. We really encourage businesses to provide and prioritize data readiness, implement appropriate strategies and tools, and continuously improve their data management practices to ensure that their data is prepared for customer engagement. I hope that you’ve enjoyed this. My name is Ryan. Please don’t forget to subscribe on whichever platform you’re on, whether you’re listening to us on Spotify or Apple podcasts, wherever it may be, or if you’re watching us on YouTube or TikTok, or wherever else it may be.
07:40
We’re all over the place and we will see you next time.