There are things in life you simply cannot afford to lose out on quality like having durable tyres for your car or sleeping on a comfortable mattress. Lacking quality in these areas can have disastrous effects in the long-run and the same can be said when it comes to the foundation of your sales and marketing revenue – B2B data.
Sales and marketing teams who strive to create high-performing campaigns must understand the importance of data quality. Without total trust and confidence in your data, any strategies to generate new business will be greatly hindered.
In this blog, we’ll cover why B2B data quality must be prioritised, how low-quality B2B data is impacting your sales revenue, and why high-quality B2B data is the answer to attaining your business goals.
But first, let’s see what quality means when it comes to B2B data.
What do we mean by ‘quality’ when talking about B2B data?
You may often see the word ‘quality’ get thrown around when describing B2B data. But what does it really mean?
Below, we’ll look at the main parameters of data quality. These will be broken down into five key points:
1. B2B Data relevancy
2. B2B Data accuracy
3. B2B Data validity
4. B2B Data breadth
5. B2B Data freshness
B2B Data relevancy
To ensure your data is relevant, you must decide who your relevant contacts and accounts are to target your sales and marketing campaigns.
Without data relevancy, you will end up targeting contacts and accounts who are not relevant for your business. Focusing on the wrong data will waste your marketing budget and misuse the time of your sales and marketing team, especially when nurturing the wrong leads.
Identifying your target market is crucial to getting high-quality B2B data. To understand who your ideal buyers are, you will need to know the criteria of your Ideal Customer Profile (ICP). This information consists of segmentable and categorical data that describes your contacts and accounts. The more specific your criteria is, the more complete and relevant your data will be.
For example, data containing only label data such as name, email address and job title minimises the level of personalisation and makes analysis one-dimensional. However, the addition of categorical data such as seniority, tenure and gender makes the data more meaningful and enables more in-depth levels of personalisation and analysis.
Relevant B2B data will make you confident in the success of your sales and marketing efforts. It enables you to accurately segment your data to create highly targeted campaigns. The content of your campaigns will speak to each of your prospects in a way that is meaningful and adds value to them.
B2B Data accuracy
Once you have decided upon who your relevant buyers are, the accuracy of the information for that data is the next step to obtaining high-quality B2B data.
One main reason this occurs is that key steps to check the data are left out. One important step is to implement a human-vetted process to visually check the data rather than blindly assuming the data is good to use.
Inaccurate data can stem from a vast array of issues and its consequences can be very damaging to your business. It can wreak havoc to your marketing efforts such as by incorrectly naming a prospect, mistargeting accounts, or unknowingly omitting prospects from campaigns.
All these instances can cause sales opportunities to be missed or lost which impacts sales revenue. Below are the many ways data can be inaccurate and how different data inaccuracies could exist in your B2B data.
Wrong spelling or typos
Spelling mistakes, spelling variations and typos are most common when entering data manually. Sending an email and incorrectly spelling the prospect’s name can be a deal-breaker. It is usually very hard to win back their trust and a simple error like this can result in a lost potential sales lead.
For example, names from LinkedIn can include emojis, upper case letters, middle names, or post-nominal letters. If you are not careful an email could very easily address the prospect as “Hey ” or “Hello Mr Smith, MBA”. This will quickly have them mark your email as spam, delete your email, and even unsubscribe to you losing out on any chance of potential future business opportunities.
Another type of error is misspelling key data points used for segmenting your data. Suppose you want to target prospects with ‘Director’ in their job title and ‘Sales’ being the department they work in. If a contact is mistakenly entered as working in the ‘Sale’ department, that contact will not be included in the ‘Sales’ segment resulting in a missed sales opportunity.
Duplicates are created when you have 2 or more records of the same contact in your B2B database. They don’t necessarily have to be exact duplicates, it may be one record only has a first name and email address but a second has a full name, email address and company information included.
Duplicate records can be caused by having multiple data sources or different people adding new data to an existing database. Without a centralised source of data and not implementing a routine data hygiene process, this can result in catastrophic consequences where the same prospect may be targeted by two separate campaigns.
Not only does this inflate your numbers in your analytics but it also makes you appear highly unprofessional. Your marketing efforts will have gone to waste and any potential business opportunities from that prospect would have evaporated which impacts overall company revenue.
B2B Data validity
When working with large sets of data and preparing it for any kind of sales or marketing campaign, it is very important to check the validity of the data before putting it to use. Data validation ensures your data has consistent formatting, contains valid contact information, and is free from errors and anomalies.
Since so much of sales and marketing is done using automation these days, it can be very easy for things to go wrong. Your efforts in marketing can go to waste if it is filled with mistakes. It may also reject certain data points and omit contacts from campaigns.
Data validation is the preventative measure that ensures your data is used effectively and that you maintain the greatest chance of generating more sales opportunities. The following are a few problems that may occur if you don’t validate your data.
This is a tricky one to amend and oftentimes must be done manually. If you use data from different sources or have different people manually add data in, this can create irregularities making your database quite messy and problematic to deal with.
An example can be seen when certain geographical locations are abbreviated. One source of data may have company location inputted as ‘United Kingdom’, whereas another source may have abbreviated the term as ‘UK’, ‘GB’ or ‘GBR’.
Another issue can be seen in job title or industry. For example, someone who works in sales may be recorded as ‘Sales Development Representative’. This can also be shortened to ‘Sales rep’ and abbreviated to ‘SDR’. Moreover, the same role can also be known as ‘Business Development Representative’ which may be referred to as ‘Biz Dev’ and abbreviated as ‘BDR’.
Furthermore, quantitative data can also be inputted in different ways. If the figure for annual revenue of a company is 20,000,000, it may be inputted as 20.000.000 or 20000000 and abbreviated as 20M.
All these different variations can make your data become difficult to manage. Without a standardised format, your data analytics can become skewed causing you to miss out on targeting potential prospects in your marketing campaigns, thus, losing out on sales revenue.
Incomplete B2B data
Whether you are creating a targeted marketing campaign or trying to segment your data, you will have to rely on certain key data points. Incomplete data occurs when data points that are omitted leaving you with gaps in your data set.
This could come from a poor data source, incomplete webforms, or missed out if inputted manually. All this can cause inefficiencies and problems such as data being used ineffectively or even wasted.
You may have a misrepresentation of the number of people you are targeting since you are unable to segment your data accurately. Also, incomplete data can cause some prospects to be excluded from your targeted campaigns resulting in missed sales opportunities.
A workaround can be set up to create a generic marketing campaign for “everyone else” who isn’t included in your targeted campaigns. However, this only results in sending weak or irrelevant messages to otherwise potential customers.
If you’re using other technologies such as outbound automation tools or a B2B CRM, you may encounter difficulty when integrating your data with your sales tech stack. What’s worse is that you may unknowingly send out messages including blank fields making your message unreadable and appear very unprofessional.
The validity of B2B contact dataThe importance of validating the contact information of your contacts and accounts ensure your marketing efforts go to plan. Whether the communication channel you use is via email, phone, social media, or post, you must validate your contact information or else you will not reach your potential buyers.
Ringing invalid phone numbers or getting email delivery failures will make your marketing and data collection efforts redundant. The whole sales process breaks and it costs your team time, disturbs workflow and can damage your email domain.
Since single-channel campaigns such as targeting your prospects via email only will limit your chance of generating opportunities, it is much more beneficial to adopt a multi-channel approach. This way you incorporate various communication channels that people often use to have the highest chance of a reply. But, you must ensure each communication channel is valid for your campaign to perform properly.
By performing data validation checks either in-house or having your data provider complete this prior to delivering your data, you can be sure you will get the most out of your data. Checking the validity of your data is a crucial step in ensuring you have high-quality data and that you have the highest chance of your campaigns generating more sales opportunities.
B2B Data breadth
Another aspect of B2B data quality is the breadth of data you have for each contact or account. For your data to be high quality, you should have a rich profile of many data points that allows for personalisation and enables you to draw deep insights into your data.
Data breadth gives you an added dimension to improving the quality of your data. Without data breadth, you will lack the information needed to tailor your marketing activities as well as limiting the ability to view trustworthy and actionable insights of your data.
This leads to poor marketing campaigns which result in sales opportunities being lost, thus, impacting overall revenue. Additionally, the inability to view actionable insights causes you to lose your competitive advantage when trying to improve the performance of your marketing campaigns.
Actionable insights empower your sales and marketing teams to create personalised content that is relevant and valuable to the prospects being targeted. Being able to tailor your marketing activities and value proposition to each market segment increases your chance of connecting with your potential customers. It also demonstrates that you understand their potential pain points to which you can provide a solution for.
B2B Data freshness
As soon as you get hold of your B2B data, it already starts to decay. These data points are never static as information can very easily change. Working with outdated data could mean time wasted in reaching out to the wrong people since they can change company, email address, job title and even name.
The rate data decays will depend upon the source of your data. For instance, if a lead is generated through a webform, the rate of decay will be slower since the data has been captured in real-time. On the other hand, if data has been procured from a B2B database, the rate of decay should be faster since the data has existed for some time already.
If you execute an email marketing campaign using data that has not had its emails validated, you can experience a number of your emails to bounce. Sending an email to a wrong or non-existing email address will result in a hard bounce. These types of delivery failures will impact the domain health and the spam rating of your subsequent emails.
Consider a scenario where you emailed 100 prospects of which you had the wrong email address for 15 of them. The consequence of those 15 hard bounces is that your data’s overall potential was decreased by 15%. On top of that, your email service provider will see that your email data quality is low and each hard bounce will negatively impact your domain’s reputation.
The real cost of low-quality B2B data to your business
Hopefully, the importance of quality in your B2B data has been demonstrated. But for those still on the fence and think it’s not such a big deal, here are a few ideas to see what the true cost of not using high-quality B2B data could be for your business.
How lower email deliverability can affect sales revenue
Briefly mentioned earlier, outdated contact information can result in having lower email deliverability. Even though many of us focus on other more exciting metrics like open rates, reply rates and click-through rates, if the prospect isn’t even getting your email, all these other metrics will be directly impacted.
Let’s explore how this affects overall revenue.
The example earlier saw 100 prospects be sent an email but 15 were to a wrong or invalid email address. With an email deliverability rate of 85%, this immediately decreases the data’s overall potential by 15%.
To compare this against a benchmark figure, at hubsell, we guarantee our customers with data accuracy of at least 95%, but we’ll stick with 95% for example purposes.
Email open rate
Using an average email open rate of 60%, the number of emails opened would be the following:
100*0.85*0.60=51 opened emails, as opposed to;
100*0.95*0.60=57 opened emails
A reduction from 57 to 51 people results in having to send over 11% more emails just to get back up to 57 opened emails.
Email reply rate
Now, suppose we have an average positive email reply rate of 5% of those who opened an email, the number of email replies received would be the following:
100*0.85*0.60*0.05=2.55 positive replies, as opposed to;
100*0.95*0.60*0.05=2.85 positive replies
Although this may seem small, this example could very easily represent the email volume of a single sales rep in just a single day.
In one month:
2000*0.85*0.6*0.05=51 positive replies, as opposed to;
2000*0.95*0.6*0.05=57 positive replies
As we can see, 6 potential deals per sales rep per month is lost when we compare email deliverability between 85% and 95%.
Lost annual revenue
To demonstrate the impact on annual sales revenue, we shall assume the average close rate is 20%. Let’s look at how much potential revenue is lost using two different company examples from losing 6 potential deals per month:
Company A with an average deal size of £10,000:
6*12*0.2*10,000=£144,000 is lost per sales rep per annum
Company B with an average deal size of £100,000:
6*12*0.2*100,000=£1,440,000 is lost per sales rep per annum
Multiplying this lost revenue by the number of sales reps gives an even greater and truer look at the scale of the potential loss. As we can see, a mere 10% difference in email deliverability has disastrous effects on annual sales revenue.
How a lack of personalisation can affect sales revenue
Personalisation plays a big factor in a successful marketing campaign. It shows the recipient that you thoughtfully targeted them and believe you can offer a solution that will help solve a potential pain point.
Your prospects are receiving numerous emails and messages every day. To stand out, you cannot afford to simply send generic content that could have been sent to anyone. Your prospects likely do not have the time to read everything they get sent and often decide whether to open an email or message purely based on the subject line and preview of the text.
The content of your message must be written specifically for each prospect which can be achieved by using your B2B data. Each job title has a different pain point, each industry faces different challenges, and every prospect has different levels of experience. Using your own personal knowledge along with these insights, you can gain a competitive edge by crafting a message that speaks to each prospect on an individual basis.
Some small personal touches to add would be to name their company in the subject line or compliment them within the first line so it appears in the preview. These alone can help to increase your email open and reply rates by a few percentage points each.
Increased email open and reply rate
Expanding on our earlier example, let’s compare how a modest 10% increase in open and reply rates will help generate more sales opportunities.
Low-quality data example:
2000*0.85*0.60*0.05=51 email replies per sales rep per month
High-quality data and personalised:
2000*0.95*0.66*0.055=68.97 email replies per sales rep per month.
As we can see, the difference has increased nearly 3-fold from 6 to 17.97 potential deals per sales rep per month being lost when email open and reply rate improves by just 10%.
Lost annual revenue
To further demonstrate the impact on annual sales revenue – still assuming the same 20% close rate – let’s look at how much potential revenue is lost using the same two company examples from losing 17.97 potential deals per month:
Company A with an average deal size of £10,000:
17.97*12*0.2*10,000=£431,280 is lost per sales rep per annum
Company B with an average deal size of £100,000:
17.97*12*0.2*100,000=£4,312,800 is lost per sales rep per annum
As we can see, the numbers get frighteningly bigger by improving email open and reply rate by just a few percentage points. Although this is just an example, very similar real-life scenarios can resemble this and we can quickly see how making small improvements at each step can exponentially improve the overall result.
Mistargeting segments can lead to wasted effort and cost
There are many ways to segment your B2B data. The most common ways would be by using a mix of industry, company size and job title.
However, you can segment much more granularly by using other forms of B2B data such as firmographics, demographics, technographics, etc.
The level of granularity will depend on the data points you have for each contact and account. By not being able to clearly profile your data into segments, you will not be able to create a quality message that resonates with your target market.
While having more data points enables you to target your prospects with greater accuracy and focus, fewer data points will keep your message generalised and appear to be a one-to-many type message as opposed to it being one-to-one and tailored for each individual.
Having full visibility of your data, you will be able to understand why a certain campaign is performing well or what segment is resonating well for you. Focusing on the best performing segments and campaigns enables you to scale your winning approach to further optimise and generate more sales.
Higher customer acquisition cost
Another cost of using low-quality B2B data is that you end up incurring a high customer acquisition cost. This could be due to time wasted on marketing to irrelevant prospects, paying fo