Data is the lifeblood of any organization dealing with the customers. Be it to understand their requirements or to stay in touch with them through marketing campaigns or to send them regular updates through mailers, businesses highly depend on a variety of data. But the shocking fact is that a lot of businesses find their data of poor quality. More harming than this is that there are businesses who even don’t know the harmful effects of poor quality data. They are completely unaware of the costs associated with poor data quality.
With the changing times and growing participation of customers in most of the business decision directly or indirectly, the importance of data quality has increased like anything. With an increasing number of marketing channels and ever-discerning customer, it has got almost impossible for the organizations to survive with poor data quality and such entities can be easily found out. It leaves a direct impact on company’s ROI and response rates of campaigns.
Reasons for poor data quality
There are a number of reasons that deteriorate the quality of data over time. Some of these can be easily controlled while others need extra attention from the point data is added to the records. Given below are some of the major reasons behind poor data quality:
Duplicate records, which not just create conflicting information but also become a hindrance for the organizations when they try to get an accurate view of the customer.
Disparate data sources: Companies usually collect data from different sources like the calling team, vendors, social media platforms, customer inquiry forms etc. This increases chances of data collection errors and poor quality of the records.
Misheard data by the client representatives: Call center staff usually work in noisy environments and clients with various accents. In such situations, chances of collecting wrong data increase manifolds. Sometimes they also mishear some terms like Shore as Shaw, Pete, and Peat, Salt and Walt etc.
Wrong entries by data entry team: Chances of errors increase where data from handwritten coupons, forms etc. are filled manually. Some people have illegible handwriting making it difficult for the data entry team to understand the initials.
Online form entry errors: Sometimes customers deliberately fill wrong information to avoid marketing messages. Phone numbers and email ids are the most common errors customers make while filling online entry forms.
The high amount of such duplicate and poor quality data, organizations are at a risk of making wrong business decisions, targeting wrong customers, running poorly performing marketing campaigns and wasting resources on additional manual data maintenance processes.
Though there are different data cleansing tools and software in the market, there are some hard-to-spot and non-exact matching duplicates. Fuzzy matching is the best solution to find such data sets and eliminate them from the records to get useful data for marketing campaigns.
There are around 16 types of fuzzy matching algorithms. Businesses need to understand them well to use the best one to improve quality of their data through deduplication process. Given below are the major algorithm types of fuzzy matching.
- Exact matching to determine two identical strings.
- PhonetEx to determine “alike-sounding” relationships between words.
- N-gram or Q-gram for statistical and natural language processing where an n-gram is a subsequence of n items from a given sequence.
- Jaro to collect common characters between two strings. It also includes counting of transpositions between the common strings.
- Dice’s coefficient is a variation of n-gram, which counts matching n-grams and discards duplicate ones.
- Jaccard similarity is again a variation of n-gram with just a different formula for calculation of similarity.
- Levenshtein is an algorithm which looks for the similarity of two strings by paying attention to the number of character mistakes. It basically focuses on keyboard typing errors.
- Longest common substring takes into consideration the longest common set of adjacent characters between two strings. It works on the formula: LCSLength/ maxLength.
- Containment returns 100 percent of a string if it is a subset of another string.
- Frequency algorithm works by matching the characters of one string to the other string without paying attention to the sequence.
- Soundex is a comparison based fuzzy matching algorithm that also includes a string transformation.
Fuzzy Matching to Prepare Clean Data for Marketing Campaigns
The technique has been around for years, but its use has increased in recent years after the innovation of software. It has made data cleansing more advanced, quick and effective, which has further enabled the organizations to have clean records to create marketing campaigns with high click-through rates. Apart from data deduplication, the businesses also use the technology in application integration projects to offer a single version of the truth.
Software options for fuzzy matching
With the ever-growing demand for advanced tools and software for data deduplication and cleansing, the options have also increased in the market. Though the presence of a variety of software tools for fuzzy matching is good, at times finalizing software gets difficult for the organizations.
If you want to invest in the software to create a list of direct mail campaigns, you should opt for specialist software that focuses on addresses and direct mail fuzzy matching.
While getting the software installed, also check that it has configuration options as per your business requirements. If you find it difficult and time-consuming, you can also take help of specialist data cleansing agencies having expertise at using different tools to create clean and effective direct mail list.
Marketing campaigns play a vital role in any business’ promotional strategies. If the mails or messages sent to the customers don’t get opened or go to their spam list, the whole purpose of performing the activity gets wasted. You can avoid such situations by ensuring that your records are clean and error free. Sometimes potential customers also get irritated by multiple emails from the same brand. Use an effective fuzzy matching tool and clean duplicate and wrong entries from your records.