Even though inbound marketing may seem like a challenge to execute, auctioneers do not need to be hesitant about implementing it as a strategy if they have accurate information. When it comes to outbound marketing, an auctioneer can get powerful results if their strategy is leveraged correctly and is grounded in proper practice.
Though the industrial auction industry is moving online, there are still those who respond best to telemarketing and inside sales. These individuals often depend heavily on trust and existing relationships when sourcing their equipment. Marketing through this method often requires a substantial expenditure of time and capital developing a network of contacts, if an existing one is not available. Lead generation may be difficult, especially if the target audience is already wary of advertising.
Despite these drawbacks, filtered telemarketing can be particularly helpful when trying to arrange a private treaty sale. It can also be beneficial when trying to sell very specialised equipment.
In the case of internal sales, a good lead nurturing program can result in long and profitable business relationships. Internal sales are still very much a part of auction marketing.
An effective internal sales campaign must be founded on a curated network of interested buyers. This network must be nurtured and updated to include new contacts. Attempting to market to a network en masse, or to one that has not frequently been contacted, should be avoided. As with internal sales, there are those who an auctioneer can best reach through flyers and advertisements in trade publications. These people may not use the Internet for business-related information or may view print media as more authoritative.
Advertising through print media can have several drawbacks in terms of cost and ROI tracking. If an auctioneer uses flyers, it can be impossible to calculate how many are accepted versus how many are discarded. Trade publications often have fees for posting in them. Even if they have an estimated number of readers, it is nearly impossible to tell how many individuals your campaign will reach. To be effective, the media consumption habits of potential bidders must inform any print media campaign.
Having a clear picture of who an auction is trying to target will determine the best type of print media to utilize. In any case, it is important to avoid over-saturation.
As previously stated, inbound marketing is a process; it is important to be patient with results and consistently monitor the effectiveness of any implemented strategy. Email allows an auctioneer to reach a large number of people in a short amount of time, for a relatively low cost.
As with print media, planning is paramount to success. As email is now a mature medium, having been used for decades, audiences are wary of the potential for spam.
With this in mind, filtered email lists and adjusted email content according to gathered data is crucial.
An auctioneer should always be paying attention to the subject lines and content of their emails, and the effect they have on open and opt-out rates. Instead of a sporadic email campaign announcing an auction to your entire contact list, try offering your potential audience useful information or a solution to their problem. You can then provide an email opt-in if readers would like to learn more.
Doing this will help establish you as a trusted source of information. Though not as direct as email, SEO is one of the most powerful ways to attract new customers to your website and auctions. By optimising for certain keywords, an auctioneer can drastically increase the numbers of visitors to their site.
Free Arts for Abused Children. Fiduciary Solutions LLC. Camelback Moving Inc. Biltmore Loan and Jewelry. ASU Athletics. My Account Login My Account. Deer Valley Dr. Suite Phoenix, AZ customerservice auctionnation. Table 4 reports some summary statistics for this sample. Each row reports the data for one of the 7 ad networks, or -the last row -for the outside option.
The following table, instead, Table 5, reports the summary statistics at advertiser level. Columns 1 - 4 refer to , columns 5 - 8 report the same values for On average, they affiliate with 0. For all these advertisers, we know their main sector of business media, financial services and food-and-beverages being the three largest. Figure 2 reports the number of advertisers for each one of the 24 industries categorized in the Redbooks data.
Finally, for a subset of advertisers, we also know information on revenues and the number of employees. Figure 3: Network Industry Specialization The discrete choice model is estimated following Train via maximum likelihood. Advertiser i choice of which network to join, or whether to take an independent agency, is modeled as a function of the characteristics of each one of these options, the characteristics of the advertiser himself and a logit error.
Figure 3 visually describes the evidence by reporting the degree of specialization between the 7 ad networks. Table 6 reports the result of the choice model. Various specifications are reported, de- pending on the different covariates for both i and j are included in x.
Indeed, advertisers rand to strictly prefer agencies handling accounts in similar markets. We consider that this might be a direct effect of how advertisement over the internet works and we will explore this channel in the next section.
Therefore, in the next section we will exploit the merger between Aegis-Dentsu and Merkle - already discussed in section 3 - to analyze the effects of increased agency concentration on the strategies used in online auctions. The former case is nearly ideal to study the effects of changes in the agency structure to auction-level outcomes, like CPC and entry.
This is the reason why all the following analysis is based on the Aegis-Dentsu-Merkle merger. VI Ad Auction Strategies: Descriptive Evidence In this section we intend to explore what are the effects produced by a change in concentration on the agency side. As discussed in section 3, Aegis-Dentsu and Merkle share many clients active on the same keywords.
Therefore this merger is ideal to study the various potential strategies that the merged entity can implement, for those clients that decide to remain with it post merger. For all such advertisers, we collect all the keywords they have been bidding on Google according to the SEMrush data. Summary statistics for this dataset are reported in Table 8. Dentsu-Merkle can take and to illustrate their quantitative relevance.
Therefore, we cluster all the keywords of Table 8 in 6 different classes, according to the strategies followed by the advertisers and of the cost-per-click dynamics after the merge.
In particular, these six keyword strategies are: exiter keywords which appear before but not after the merge , entrant keywords appearing only after the merge and stayer keywords appearing in both periods. A further distinction is made according to the number of advertisers bidding on the keywords: these are either shared - i.
Table 9 summarizes the number of keyword present in each of the six, mutually exclusive, basic strategies. Below we display a series of time trend graphs of various quantities, by month, on different keyword classes and sample sizes.
In particular, samples are clustered according to the presence of the keyword in our data before these are the exit keywords , after these are the entry , or both stay in SEM data regarding DentsuAegisMerkle advertisers. Exit and entry keywords are divided in shared and non-shared - columns 1 and 2 - while Stay keywords are divided according to the cpc dynamics - either increasing or decreasing.
Figure 4 highlights the CPC dynamics of stay keywords from July, until January, for various subsamples - , , and all top volume keywords. In figure 5 a similar picture is reported for exit and entry keywords in left and right panels, respectively. Interestingly, the average CPC shows an increasing trend for exit shared keywords in the aftermath of the merge - i.
Finally, figures 6 and 7 show the effects of the merger on the average number of bidders and on the total number of bids for stay keywords. Figure 5: Entry and Exit keywords dynamics Notes: Panel a : weighted average cpc in best exit keywords; panel b weighted average cpc in best entry keywords; panel c weighted average cpc in all exit keywords; panel d weighted average cpc in all entry keywords.
Figure 7: Stay keywords: Total Number of Bids Notes: Panel a : total number of bids in best stay keywords; panel b total number of bids in best stay keywords; panel c total number of bids in best stay keywords; panel d total number of bids in all stay keywords.
The discussion in the previous section spelled out in a generic way the differences-in-differences method that we plan to follow. Mapping that general idea to concrete steps capable of effectively delivering reliable estimates of the effects of common agency requires substantial care. Two features are particularly salient. The first involves when can we exploit the emergence of a case of common agency to study bidding behavior.
The second involves the construction of the control group. Regarding the first issue, not all instances of common agency that arises in our data are appropriate to address the question of interest. However, questions regarding why did such an advertiser join the DMA would naturally arise. The complexities of bidding in the ad auctions have pushed over time the advertisers that were considering their ad campaigns not optimally run to delegate bidding to DMAs more expert in optimizing bids.
But then if the source of common agency is a failure to optimally bid of one of the members of the agency in the pre- common agency period, then our difference-in-differences analysis would produce estimates that convolute together bid changes arising from both collusion and failure to optimize. Therefore, the ideal setting for our analysis requires looking at advertisers that are most likely already bidding optimally before the creation of the common agency. This condition is satisfied for advertisers that are already delegating their bids to DMAs.
However, even in this case the switch from one DMA to another operated by an advertiser might signal the presence of elements that would limit the validity of our empirical strategy. As discussed above, both agencies are highly sophis- ticated players and, therefore, the bids submitted for their clients can reasonably considered as optimal bids. This merger produced instances of common agencies in a broad range of extremely relevant markets.
Second, a crucial element deserving close attention is the construction of a reliable control group of keywords. Furthermore, we shall consider with care all the possible cases of keywords that were shared only for a subset of the months analyzed either before or after the merger. However, the large number of available keywords, as well as specific market features like the one described above, make us confident that this task is feasible.
In this figure, we consider a keyword as shared if there is common bidding for at least one month, but clearly this definition could be made more or less stringent and it will be interesting to explore to what extent the results will be affected by this margin of choice.
Continuing with the Dell example, the next margin of choice is whether to use all the shared keywords several thousands in this specific case , or to focus on a more restricted set. Given the risk of excessive noise by selecting marginal keywords, we see as ideal to perform our analysis on a more restricted set of keywords.
More specifically, we plan to focus on four different subgroups: the set of top 30, 50, , keywords by traffic volume. The set of top 10 keywords and their relevant summary statistics are reported in Table From this table we can immediately see that for several keywords their CPC goes down in the six months after the merger, relative to the six months before the merger.
Most auction companies consider the marketing job complete when the last ad runs. We understand that there is much more to generating buyers than running a few ads. In addition, we take a proactive role in telephoning and emailing potential buyers and encouraging their attendance. Auctions are news-worthy events and broadcasting that message to news media outlets will produce crucial exposure to the public. The press release will contain news-worthy headlines announcing the auction, as well as, specific details about the property grounds, terms of the auction, and information where buyers can learn more about the auction.
Our staff will prepare a comprehensive information packet on your property that will be given to potential buyers to assist them in their due diligence and investigation of your property.
Information in the packet would include general information on the property such as: Taxes, deeds and copies of zoning hand books, aerial photos, maps, plot plans, surveys, utility information, auction terms and conditions, recommended financial and settlement institutions, and more.
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